Biography

My research topics:
- Weed management with reduced environmental impact
- Integrated weed management in field vegetables
- Site-specifc weed management
- Precision farming and robotics
- Control of alien invasive plants

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Abstract

Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.

Abstract

Reusing soil can reduce environmental impacts associated with obtaining natural fresh soil during road construction and analogous activities. However, the movement and reuse of soils can spread numerous plant diseases and pests, including propagules of weeds and invasive alien plant species. To avoid the spread of barnyardgrass in reused soil, its seeds must be killed before that soil is spread to new areas. We investigated the possibility of thermal control of barnyardgrass seeds using a prototype of a stationary soil steaming device. One Polish and four Norwegian seed populations were examined for thermal sensitivity. To mimic a natural range in seed moisture content, dried seeds were moistened for 0, 12, 24, or 48 h before steaming. To find effective soil temperatures and whether exposure duration is important, we tested target soil temperatures in the range 60 to 99 C at an exposure duration of 90 s (Experiment 1) and exposure durations of 30, 90, or 180 s with a target temperature of 99 C (Experiment 2). In a third experiment, we tested exposure durations of 90, 180, and 540 s at 99 C (Experiment 3). Obtaining target temperatures was challenging. For target temperatures of 60, 70, 80, and 99 C, the actual temperatures obtained were 59 to 69, 74 to 76, 77 to 83, and 94 to 99 C, respectively. After steaming treatments, seed germination was followed for 28 d in a greenhouse. Maximum soil temperature affected seed germination, but exposure duration did not. Seed premoistening was of influence but varied among temperatures and populations. The relationships between maximum soil temperature and seed germination were described by a common dose–response function. Seed germination was reduced by 50% when the maximum soil temperature reached 62 to 68 C and 90% at 76 to 86 C. For total weed control, 94 C was required in four populations, whereas 79 C was sufficient in one Norwegian population.

To document

Abstract

Many herbaceous perennial plant species gain significant competitive advantages from their underground creeping storage and proliferation organs (CR), making them more likely to become successful weeds or invasive plants. To develop efficient control methods against such invasive or weedy creeping perennial plants, it is necessary to identify when the dry weight minimum of their CR (CR DWmin) occurs. Moreover, it is of interest to determine how the timing of CR DWmin differs in species with different light requirements at different light levels. The CR DWmin of Aegopodium podagraria, Elymus repens and Sonchus arvensis were studied in climate chambers under two light levels (100 and 250 μmol m−2 s−1), and Reynoutria japonica, R. sachaliensis and R. × bohemica under one light level (250 μmol m−2 s−1). Under 250 μmol m−2 s−1, the CR DWmin occurred before one fully developed leaf in R. sachaliensis, around 1–2 leaves in A. podagraria and E. repens and around four leaves in S. arvensis, R. japonica and R. × bohemica. In addition to reducing growth in all species, less light resulted in a higher shoot mass fraction in E. repens and S. arvensis, but not A. podagraria; and it delayed the CR DWmin in E. repens, but not S. arvensis. Only 65% of planted A. podagragra rhizomes produced shoots. Beyond the CR DWmin, Reynoutria spp. reinvested in their old CR, while the other species primarily produced new CR. We conclude that A. podagraria, R. sachaliensis and E. repens are vulnerable to control efforts at an earlier developmental stage than S. arvensis, R. japonica and R. × bohemica.

To document

Abstract

The adoption of site-specific weed management (SSWM) technologies by farmers is not aligned with the scientific achievements in this field. While scientists have demonstrated significant success in real-time weed identification, phenotyping and accurate weed mapping by using various sensors and platforms, the integration by farmers of SSWM and weed phenotyping tools into weed management protocols is limited. This gap was therefore a central topic of discussion at the most recent workshop of the SSWM Working Group arranged by the European Weed Research Society (EWRS). This insight paper aims to summarise the presentations and discussions of some of the workshop panels and to highlight different aspects of weed identification and spray application that were thought to hinder SSWM adoption. It also aims to share views and thoughts regarding steps that can be taken to facilitate future implementation of SSWM.

To document

Abstract

This study provides a multi-attribute approach to support decisions by Norwegian crop farmers considering adopting innovative crop protection measures. In modelling choice among pest management strategies, we have accounted for both economic risks, risks to human health and risks to the environment. We used the Simple Multi-Attribute Rating Technique (SMART) to evaluate the results of a field trial comparing four different pest management strategies. In the trial, various pre-crops in year one were followed by two consecutive years of winter wheat. Two treatments had different levels of integrated pest management (IPM). IPM1 was the most innovative treatment and used less pesticides (i.e. herbicides, insecticides and fungicides) than IPM2. The third treatment (‘Worst Case’, WC) used pesticides routinely. The fourth treatment (‘No Plant Protection’, NPP) used no plant protection measures except one reduced dose of herbicide per year on winter wheat. Two main attributes were included in the SMART analysis, an economic indicator and a pesticide load indicator, each of which comprised a number of attributes at a subsidiary level. The results showed that the IPM1 and NPP strategies performed better than IPM2 and the WC strategies. However, the ranking of the pest management practices depended on the weighting of the two main attributes. Although the SMART analysis gave ordinal utility values, permitting only ranking of the alternatives, we were able to transform the results to measure financial differences between the alternatives.

To document

Abstract

Ornamental jewelweed (Impatiens glandulifera Royle) is an alien invasive plant in Europe. This annual plant often grows in riparian habitats where herbicides are prohibited. Several studies have reported the negative effect on ecosystem and ecosystem services by this species. However, limited research is published on control measures and the aim of our study was to explore use of hot water and cutting to control I. glandulifera. A lab experiment showed that the lethal water temperature for seed was between 45 and 50 C. In a pot experiment with seeds in soil, emergence of I. glandulifera was reduced by 78% and 93% compared with the untreated control with volumes of hot water (80 C) of 7.2 and 14.5 L m−2, respectively. When treatments were conducted on relatively tall plants (almost 60 cm) in late June, hot water gave significantly better control than cutting. Compared with an untreated control, I. glandulifera cover was reduced by 97% and 79% after hot water and cutting, respectively. Application of hot water to smaller (<40 cm) and less developed plants (BBCH 12–13) in early June and cutting of plants with visible flower buds (mid-July) led to no significant difference in cover. Compared with an untreated control, I. glandulifera cover was reduced by 99% (cut below first node) and 91% (hot water and cut above first node). When relatively tall plants (almost 60 cm) were treated, hot water use was high (31.1 L m−2) and required twice as many work hours (4.8 min m−2) as cutting (2.4 min m−2). When smaller plants (<40 cm) were targeted, work hours and hot water use were reduced to 2.1 min m−2 and 13.7 L m−2, respectively.

To document

Abstract

Docks (Rumex spp.) are a considerable problem in grassland production worldwide. We investigated how different cultural management techniques affected dock populations during grassland renewal: (I) renewal time, (II) companion crop, (III) false seedbed, (IV) taproot cutting (V), plough skimmer and (VI) ploughing depth. Three factorial split-split plot experiments were carried out in Norway in 2007–2008 (three locations), 2008–2009 (one location) and 2009 (one location). After grassland renewal, more dock plants emerged from seeds than from roots. Summer renewal resulted in more dock seed and root plants than spring renewal. Adding a spring barley companion crop to the grassland crop often reduced dock density and biomass. A false seedbed resulted in 71% fewer dock seed plants following summer renewal, but tended to increase the number of dock plants after spring renewal. In some instances, taproot cutting resulted in less dock biomass, but the effect was weak and inconsistent, and if ploughing was shallow (16 cm) or omitted, it instead increased dock root plant emergence. Fewer root plants emerged after deep ploughing (24 cm) compared to shallow ploughing, and a plough skimmer tended to reduce the number further. We conclude that a competitive companion crop can assist in controlling both dock seed and root plants, but it is more important that the renewal time is favourable to the main crop. Taproot cutting in conjunction with ploughing is not an effective way to reduce dock root plants, but ploughing is more effective if it is deep and a skimmer is used.

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Abstract

Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes systematic growing techniques to navigate and operate in the field. By the use of machine vision it separates seeded vegetable crops from weed. Each weed within the row is treated with individual herbicide droplets, without affecting the crop. This results in a significant reduction in herbicide use, and allows for the use of herbicides that would otherwise harm the crop. The robot is tailored to this purpose with cost, maintainability, efficient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benefit of reduced weight, complexity and cost. Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 μg glyphosate or 0.15 μg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for droplet stability and leaf retention properties. The common herbicide glyphosate had no effect unless mixed with suitable additives. A field trial with the robot was performed in a carrot field, and all the weeds were effectively controlled with the DoD system applying 5.3 μg of glyphosate per droplet. The robot and DoD system represent a paradigm shift to the environmental impact and health risks of weed control, while providing a valuable tool to the producers.

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Abstract

The success of precision agriculture relies largely on our ability to identify how the plants’ growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11–20 DAS. Differentiation of more than one stress factors with the same index was difficult.

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Abstract

Arable weeds are generally distributed in patches, while herbicides are applied uniformly. Herbicides can be saved if only the patches are sprayed, i.e. patch spraying (PS). Bottlenecks for cost-effective PS are weed monitoring technology and valid technology-based decision rules for PS (thresholds). The novel machine vision algorithm Weedcer has been developed as an efficient weed monitoring tool for PS. Weedcer estimates the proportions of young weed leaves and cereal leaves in high resolution red–green–blue images. We conducted field trials to test relative weed cover (RWC) and relative mayweed cover (RMC) estimated by Weedcer as decision rules for PS. RWC is the total weed cover divided by the total plant cover and RMC is the mayweed cover divided by the total plant cover. The main criterion for evaluation and basis of these thresholds was the measured grain yield. Images (about 0.06-m2) were acquired with a GPS guided autonomous field robot in spring, the normal time for spraying seed-propagated broadleaf weeds in both winter – and spring cereals in Norway. Three map-based trials (weed monitoring and spraying in two separate operations) showed that mean RWC per management unit (12.0 × 12.5-m) was generally adequate. In winter wheat heavily infested with scentless mayweed (Tripleurospermum inodorum (L.) Sch.Bip.) and/or scented mayweed (Matricaria recutita L.), the mean RMC per management unit was more adequate. Progress during the project allowed three additional trials conducted in real-time (weed monitoring and spraying in the same operation). These were conducted with the robot in spring cereals, and showed that a weighted moving average of RWC per image was adequate. The sprayed and unsprayed management units in these trials were minimum 3.0 × 3.0-m and 0.5 × 3.0-m, respectively. Results indicated that the Weedcer-based thresholds should be lower in wheat (Triticum aestivum) than in barley (Hordeum vulgare).

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Abstract

Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm ("WeedFinder") estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red-green-blue images. The ability of "WeedFinder" to predict 'spray'/'no spray' decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, "WeedFinder" gave correct spray decisions in 65-85% of the test images. With discriminant analysis, corresponding mean rates were 84-90%. Future versions of "WeedFinder" must be more accurate and accommodate weed species recognition.

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Abstract

A possible cost-effective real-time patch spraying implementation against seed-propagated broad-leaved weeds in cereals is a camera mounted in front of the tractor taking images at feasible distances in the direction of travel, on-board image analysis software and entire boom switched on and off. To assess this implementation, manual weed counts (0.25 m(2) quadrats) in a 1.5 m x 2 m grid, were used to simulate camera outputs. Each quadrat was classified into 'spray' and 'not spray' decisions based on a threshold model, and the resulting map defined the 'ground truth'. Subsequently, 'on/off' spraying at larger control areas where sizes were given by the boom width and image distance, and spraying decision controlled by weed status at the single quadrat simulating the camera's view, were simulated. These coarser maps were compared with 'ground truth', to estimate mapping error (area above threshold not sprayed), spraying error (area below threshold sprayed), total error (sum of mapping and spraying error) and the herbicide reduction. Three levels of the threshold model were tested. Results were used to fit models that predict errors from boom width and image distance. Size of control area did not on average affect the magnitude of the simulated herbicide reductions, but the bigger the control area the higher the risk that the simulated herbicide reduction deviate from the reduction in 'ground truth'. Mean simulated herbicide reductions were 42-59%, depending on threshold level. Only minor differences due to threshold level were seen for mean mapping and spraying errors at given spraying resolutions. Using original threshold level and image distance 2 m, predicted total errors for boom widths 2 m, 6 m, 20 m and 40 m would be 6%, 10%, 16% and 17%, respectively. Results indicate that control area should not exceed about 10 m 2 if acceptable total error is maximum 10%.

Abstract

Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.

Abstract

Weeds may reduce crop yields significantly if managed improperly. However, excessive herbicide use increases risk of unwanted effects on ecosystems, humans and herbicide resistance development. Weed harrowing is a traditional method to manage weeds mechanically in organic cereals but could also be used in conventional production. The weed control efficacy of weed harrowing can be adjusted by e.g. the angle of the tines. Due to its broadcast nature (both crop and weed plants are disturbed), weed harrowing may have relatively poor selectivity (i.e. small ratio between weed control and crop injury). To improve selectivity, a sensor-based model which takes into account the intra-field variation in weediness and “soil density” in the upper soil layer (draft force of tines), is proposed. The suggested model is a non-linear regression model with three parameters and was based on five field trials in spring barley in SE Norway. The model predicts the optimal weed harrowing intensity (in terms of the tine angle) from the estimated total weed cover and SD per sub-field management unit, as well as a pre-set biological weed threshold (defined as the acceptable total weed cover left untreated). Weed cover and SD were estimated with RGB images (analysed with custom-made machine vision) and an electronic load cell, respectively. With current parameter values, the model should be valid for precision weed harrowing in spring barley in SE Norway. The next step is to test the model, and if successful, adjust it to more cereal species. Weeds may reduce crop yields significantly if managed improperly. However, excessive herbicide use increases risk of unwanted effects on ecosystems, humans and herbicide resistance development. Weed harrowing is a traditional method to manage weeds mechanically in organic cereals but could also be used in conventional production. The weed control efficacy of weed harrowing can be adjusted by e.g. the angle of the tines. Due to its broadcast nature (both crop and weed plants are disturbed), weed harrowing may have relatively poor selectivity (i.e. small ratio between weed control and crop injury). To improve selectivity, a sensor-based model which takes into account the intra-field variation in weediness and “soil density” in the upper soil layer (draft force of tines), is proposed. The suggested model is a non-linear regression model with three parameters and was based on five field trials in spring barley in SE Norway. The model predicts the optimal weed harrowing intensity (in terms of the tine angle) from the estimated total weed cover and SD per sub-field management unit, as well as a pre-set biological weed threshold (defined as the acceptable total weed cover left untreated). Weed cover and SD were estimated with RGB images (analysed with custom-made machine vision) and an electronic load cell, respectively. With current parameter values, the model should be valid for precision weed harrowing in spring barley in SE Norway. The next step is to test the model, and if successful, adjust it to more cereal species.

Abstract

Weed-free zone underneath apple trees is important to maximize vegetative growth, fruit yield- and quality. Glyphosate applied twice is the usual strategy in apple orchards in Norway. Due to uncertain future of glyphosate there is a need for alternative strategies. A field trial was conducted during 2021 in an orchard (three-year-old trees). Five alternative strategies were tested: 1) Hot water at 3 L m-2 x 3 (spring, early summer, summer), 2) Hot water at 6 L m-2 x 3 (times as previous), 3) Pelargonic acid at full dose (10.9 kg a.s. ha-1) x 1 (early summer), 4) Pelargonic acid at half dose (5.44 kg a.s. ha-1) x 2 (spring, early summer), and 5) Rotary hoe x 3 (early spring, early summer, summer). Glyphosate at 1.08 kg a.s. ha-1 x 2 (early summer, summer) was included as reference strategy. Hot water (about 80 C, 0.1 bar) was applied with a commercial machine (Heatweed Technologies, Norway). Visual assessments of percentage of ground covered by living vegetation were used to estimate weed control efficacy. Dominating species were Taraxacum officinale, Tripleurospermum inodorum, Poa annua, Polygonum aviculare, Galium aparine, Viola arvensis and Senecio vulgaris. Assessed mid-summer (June 24), hot water applied twice (both 3 L m-2 and 6 L m-2) showed very high efficacies, both about 90%. Pelargonic acid showed rather low efficacies, about 15% (10.9 kg a.s. ha-1 x 1) and 45% (5.44 kg a.s. ha-1 x 2). Rotary hoe twice had almost 60%. Efficacy of glyphosate once was 75%. The last assessment was conducted in mid-July, i.e. about 1-2 weeks after the last application of hot water, rotary hoe and glyphosate. The two hot water strategies resulted in very good weed control, i.e.

To document

Abstract

Precision weeding or site-specific weed management (SSWM) take into account the spatial distribution of weeds within fields to avoid unnecessary herbicide use or intensive soil disturbance (and hence energy consumption). The objective of this study was to evaluate a novel machine vision algorithm, called the ‘AI algorithm’ (referring to Artificial Intelligence), intended for post-emergence SSWM in cereals. Our conclusion is that the AI algorithm should be suitable for patch spraying with selective herbicides in small-grain cereals at early growth stages (about two leaves to early tillering). If the intended use is precision weed harrowing, in which also post-harrow images can be used to control the weed harrow intensity, the AI algorithm should be improved by including such images in the training data. Another future goal is to make the algorithm able to distinguish weed species of special interest, for example cleavers (Galium aparine L.).

Abstract

Reusing soil can reduce environmental impacts associated with obtaining natural fresh soil during road construction and analogous activities. However, the movement and reuse of soils can spread numerous plant diseases and pests, including propagules of weeds and invasive alien plant species. To avoid the spread of barnyardgrass in reused soil, its seeds must be killed before that soil is spread to new areas. We investigated the possibility of thermal control of barnyardgrass seeds using a prototype of a stationary soil steaming device. One Polish and four Norwegian seed populations were examined for thermal sensitivity. To mimic a natural range in seed moisture content, dried seeds were moistened for 0, 12, 24, or 48 h before steaming. To find effective soil temperatures and whether exposure duration is important, we tested target soil temperatures in the range 60 to 99 C at an exposure duration of 90 s (Experiment 1) and exposure durations of 30, 90, or 180 s with a target temperature of 99 C (Experiment 2). In a third experiment, we tested exposure durations of 90, 180, and 540 s at 99 C (Experiment 3). Obtaining target temperatures was challenging. For target temperatures of 60, 70, 80, and 99 C, the actual temperatures obtained were 59 to 69, 74 to 76, 77 to 83, and 94 to 99 C, respectively. After steaming treatments, seed germination was followed for 28 d in a greenhouse. Maximum soil temperature affected seed germination, but exposure duration did not. Seed premoistening was of influence but varied among temperatures and populations. The relationships between maximum soil temperature and seed germination were described by a common dose–response function. Seed germination was reduced by 50% when the maximum soil temperature reached 62 to 68 C and 90% at 76 to 86 C. For total weed control, 94 C was required in four populations, whereas 79 C was sufficient in one Norwegian population.

Abstract

Established invasive alien plant species make it difficult and costly to move and make use of infested soil in public and private construction work. Stationary soil steaming as a non-chemical control method has the potential to disinfect soil masses contaminated with seeds and other propagative plant materials. The outcome can vary depending on steaming temperature and duration. Higher temperatures and longer durations are relatively more efficient but may also have side-effects including change in soil physical and chemical characteristics. Barnyard grass (Echinochloa crus-galli) is among troublesome invasive species in Norway. We have tested different steam duration at 99°C to find the possible lowest effective exposure duration for killing seeds of this annual grass species. Four replications of 40 barnyard grass dry seeds of one population were placed in polypropylene-fleece bags (9*7 cm), moistened for 12 hours, and covered by the soil at a depth of 7 cm in 60*40*20 cm boxes. The boxes with soil and bags were steamed at 99°C for 1.5, 3 and 9 min. The bags including steamed seeds were taken out, opened, placed on soil surface in pots and covered by a thin layer of soil. The pots were placed in greenhouse and watered from below and seed germination was followed for a month. Moistened non-steamed seeds were used as control. It was shown that steaming at 99°C gave 0% germination indicating that 100% of the seeds were killed regardless of exposure duration while in the control seed germination was 100%. Consequently, to achieve an efficacy of 100%, exposure duration of 1.5 min would be enough. Finding the lowest possible steam temperature and exposure duration to get the highest possible seed killing in a seed mixture of different plant species as well as other factors to increase the heat transferability are under investigation. Keywords: Echinochloa crus-galli; Resource recovery; Steaming temperature and duration; Thermal soil disinfection

To document

Abstract

Many herbaceous perennial plant species gain significant competitive advantages from their underground creeping storage and proliferation organs (CR), making them more likely to become successful weeds or invasive plants. To develop efficient control methods against such invasive or weedy creeping perennial plants, it is necessary to identify when the dry weight minimum of their CR (CR DWmin) occurs. Moreover, it is of interest to determine how the timing of CR DWmin differs in species with different light requirements at different light levels. The CR DWmin of Aegopodium podagraria, Elymus repens and Sonchus arvensis were studied in climate chambers under two light levels (100 and 250 μmol m−2 s−1), and Reynoutria japonica, R. sachaliensis and R. × bohemica under one light level (250 μmol m−2 s−1). Under 250 μmol m−2 s−1, the CR DWmin occurred before one fully developed leaf in R. sachaliensis, around 1–2 leaves in A. podagraria and E. repens and around four leaves in S. arvensis, R. japonica and R. × bohemica. In addition to reducing growth in all species, less light resulted in a higher shoot mass fraction in E. repens and S. arvensis, but not A. podagraria; and it delayed the CR DWmin in E. repens, but not S. arvensis. Only 65% of planted A. podagragra rhizomes produced shoots. Beyond the CR DWmin, Reynoutria spp. reinvested in their old CR, while the other species primarily produced new CR. We conclude that A. podagraria, R. sachaliensis and E. repens are vulnerable to control efforts at an earlier developmental stage than S. arvensis, R. japonica and R. × bohemica.

To document

Abstract

The adoption of site-specific weed management (SSWM) technologies by farmers is not aligned with the scientific achievements in this field. While scientists have demonstrated significant success in real-time weed identification, phenotyping and accurate weed mapping by using various sensors and platforms, the integration by farmers of SSWM and weed phenotyping tools into weed management protocols is limited. This gap was therefore a central topic of discussion at the most recent workshop of the SSWM Working Group arranged by the European Weed Research Society (EWRS). This insight paper aims to summarise the presentations and discussions of some of the workshop panels and to highlight different aspects of weed identification and spray application that were thought to hinder SSWM adoption. It also aims to share views and thoughts regarding steps that can be taken to facilitate future implementation of SSWM.

To document

Abstract

This study provides a multi-attribute approach to support decisions by Norwegian crop farmers considering adopting innovative crop protection measures. In modelling choice among pest management strategies, we have accounted for both economic risks, risks to human health and risks to the environment. We used the Simple Multi-Attribute Rating Technique (SMART) to evaluate the results of a field trial comparing four different pest management strategies. In the trial, various pre-crops in year one were followed by two consecutive years of winter wheat. Two treatments had different levels of integrated pest management (IPM). IPM1 was the most innovative treatment and used less pesticides (i.e. herbicides, insecticides and fungicides) than IPM2. The third treatment (‘Worst Case’, WC) used pesticides routinely. The fourth treatment (‘No Plant Protection’, NPP) used no plant protection measures except one reduced dose of herbicide per year on winter wheat. Two main attributes were included in the SMART analysis, an economic indicator and a pesticide load indicator, each of which comprised a number of attributes at a subsidiary level. The results showed that the IPM1 and NPP strategies performed better than IPM2 and the WC strategies. However, the ranking of the pest management practices depended on the weighting of the two main attributes. Although the SMART analysis gave ordinal utility values, permitting only ranking of the alternatives, we were able to transform the results to measure financial differences between the alternatives.

To document

Abstract

Ornamental jewelweed (Impatiens glandulifera Royle) is an alien invasive plant in Europe. This annual plant often grows in riparian habitats where herbicides are prohibited. Several studies have reported the negative effect on ecosystem and ecosystem services by this species. However, limited research is published on control measures and the aim of our study was to explore use of hot water and cutting to control I. glandulifera. A lab experiment showed that the lethal water temperature for seed was between 45 and 50 C. In a pot experiment with seeds in soil, emergence of I. glandulifera was reduced by 78% and 93% compared with the untreated control with volumes of hot water (80 C) of 7.2 and 14.5 L m−2, respectively. When treatments were conducted on relatively tall plants (almost 60 cm) in late June, hot water gave significantly better control than cutting. Compared with an untreated control, I. glandulifera cover was reduced by 97% and 79% after hot water and cutting, respectively. Application of hot water to smaller (<40 cm) and less developed plants (BBCH 12–13) in early June and cutting of plants with visible flower buds (mid-July) led to no significant difference in cover. Compared with an untreated control, I. glandulifera cover was reduced by 99% (cut below first node) and 91% (hot water and cut above first node). When relatively tall plants (almost 60 cm) were treated, hot water use was high (31.1 L m−2) and required twice as many work hours (4.8 min m−2) as cutting (2.4 min m−2). When smaller plants (<40 cm) were targeted, work hours and hot water use were reduced to 2.1 min m−2 and 13.7 L m−2, respectively.

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Abstract

Docks (Rumex spp.) are a considerable problem in grassland production worldwide. We investigated how different cultural management techniques affected dock populations during grassland renewal: (I) renewal time, (II) companion crop, (III) false seedbed, (IV) taproot cutting (V), plough skimmer and (VI) ploughing depth. Three factorial split-split plot experiments were carried out in Norway in 2007–2008 (three locations), 2008–2009 (one location) and 2009 (one location). After grassland renewal, more dock plants emerged from seeds than from roots. Summer renewal resulted in more dock seed and root plants than spring renewal. Adding a spring barley companion crop to the grassland crop often reduced dock density and biomass. A false seedbed resulted in 71% fewer dock seed plants following summer renewal, but tended to increase the number of dock plants after spring renewal. In some instances, taproot cutting resulted in less dock biomass, but the effect was weak and inconsistent, and if ploughing was shallow (16 cm) or omitted, it instead increased dock root plant emergence. Fewer root plants emerged after deep ploughing (24 cm) compared to shallow ploughing, and a plough skimmer tended to reduce the number further. We conclude that a competitive companion crop can assist in controlling both dock seed and root plants, but it is more important that the renewal time is favourable to the main crop. Taproot cutting in conjunction with ploughing is not an effective way to reduce dock root plants, but ploughing is more effective if it is deep and a skimmer is used.

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Abstract

Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes systematic growing techniques to navigate and operate in the field. By the use of machine vision it separates seeded vegetable crops from weed. Each weed within the row is treated with individual herbicide droplets, without affecting the crop. This results in a significant reduction in herbicide use, and allows for the use of herbicides that would otherwise harm the crop. The robot is tailored to this purpose with cost, maintainability, efficient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benefit of reduced weight, complexity and cost. Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 μg glyphosate or 0.15 μg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for droplet stability and leaf retention properties. The common herbicide glyphosate had no effect unless mixed with suitable additives. A field trial with the robot was performed in a carrot field, and all the weeds were effectively controlled with the DoD system applying 5.3 μg of glyphosate per droplet. The robot and DoD system represent a paradigm shift to the environmental impact and health risks of weed control, while providing a valuable tool to the producers.

Abstract

Creeping perennial weeds are of major concern in organically grown cereals. In the present study, the effects of different timing of mouldboard ploughing with or without a preceding stubble cultivation period, on weeds and spring cereals were studied. The experiments were conducted at two sites in Norway during a two and three-year period, respectively, with the treatments repeated on the same plots. The soil cultivation treatments were a stubble disc-harrowing cultivation period followed by mouldboard ploughing and only mouldboard ploughing. The timing of the treatments were autumn or spring. The density and biomass of the aboveground shoots of Cirsium arvense (L.) Scop., Elymus repens (L.) Gould, Sonchus arvensis L. and Stachys palustris L. as well as the total aboveground biomass of the spring cereal crop (oats) were assessed. The control efficiency of C. arvense and S. arvensis was closely related to timing of the cultivation treatments. Cultivation in spring decreased the population of C. arvense and S. arvensis compared to autumn cultivation. For E. repens, timing of the treatments had no significant effect: the important factor was whether stubble cultivation was carried out (best control) or not. The overall best strategy for controlling the present perennial weed population was stubble cultivation followed by ploughing in spring. However, the associated relative late sowing of the spring cereal crop and lowered crop biomass, were important drawbacks.

Abstract

With the Directive 2009/128/EC on sustainable use of pesticides, reductions in herbicide use is a European target. The aim of this study was to compare the fi eld-specifi c herbicide use resulting from simulated integrated weed management (IWM) with farmer’s actual use. Two IWM tools applicable for cereals were explored: VIPS – a web-based decision support system, and DAT sensor – a precision farming technology for patch spraying. VIPS (adaptation of Danish “Crop Protection Online”) optimizes herbicide – and dose to weed species densityand growth stage (including ALS-herbicide resistant populations), temperature, expected yield, cereal species- and growth stage. Weeds were surveyed (0.25 m2, n=23-31) prior to post-emergence spraying in spring 2013 (six fi elds) and 2014 (eight fi elds). DAT sensor enables automatic patch spraying of annual weeds within cereals. It consists of an RGB camera and custom-made image analysis. DAT sensor acquired more than 900 images (0.06 m2) per fi eld. Threshold for simulated patch spraying was relative weed cover (weed cover/ total vegetation cover) = 0.042. Treatment frequency index (TFI, actual dose/maximum approved dose summed for all herbicides) was calculated. Without resistance strategy, average TFI for VIPS was higher for winter wheat (0.96) than for spring cereals (0.38). Spring cereal fi elds with resistance strategies gave an average TFI of 1.45. Corresponding TFI for farmer’s applications were 1.40, 0.90 and 1.26, respectively. For one fi eld wherein both tools were explored in 2013 and 2014, TFI values for VIPS were 1.86 and 1.50 due to resistant Stellaria media, while TFI for farmer’s sprayings were around 1.00. DAT sensor simulated herbicide savings of 69% and 99%, corresponding to TFI values of 0.58 and 0.01, respectively. As measured by TFI, DAT sensor showed a higher potential in herbicide savings than VIPS. VIPS is available without costs to end-users today, while DAT sensor represents a future tool.

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Abstract

The success of precision agriculture relies largely on our ability to identify how the plants’ growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11–20 DAS. Differentiation of more than one stress factors with the same index was difficult.

Abstract

In Europe there is an on-going process on implementing regulations aimed at reducing pollution from agricultural production systems, i.e. the Water Framework Directive and the Framework Directive for Sustainable Use of Pesticides. At the same time, there is an increasing focus on food security possibly leading to continued intensification of agricultural production with increased use of external inputs, such as pesticides and fertilizers. Application of sustainable production systems can only be achieved if they balance conflicting environmental and economic effects. In Norway, cereal production is of large importance for food security and reduction of soil and phosphorus losses, as well as pesticide use and leaching/runoff in the cereal production are of special concern. Therefore, we need to determine the most sustainable and effective strategies to reduce loss of top soil, phosphorus and pesticides while maintaining cereal yields. A three-year research project, STRAPP, is addressing these concerns. A catchment area dominated by cereal production is our common research arena within STRAPP. Since 1992 a database (JOVA) with data for soil erosion, nutrient and pesticide leaching/runoff (i.e. concentrations in stream water), yield, and agricultural management practices (fertilization, use of pesticides, soil tillage and rotations) has been established for this catchment allowing us to compare a unique diversity in cropping strategies in a defined location. An important part of STRAPP focuses on developing ‘best plant protection strategies’ for cereal fields in the study area, based on field inventories (manual and sensor based) of weeds and common diseases, available forecast systems, and pesticide leaching risk maps. The results of field studies during the growing seasons of 2013 and 2014 will be presented, with a focus on possible integrated pest management (IPM) strategies for weeds and fungal diseases in cereal production. We will also present the project concept and methods for coupling optimized plant protection strategies to (i) modelling of phosphorus and pesticide leaching/runoff, as well as soil loss, and (ii) farm-economic impacts and adaptations. Further, methods for balancing the conflicting environmental and economic effects of the above practices, and the evaluation of instruments for increased adoption of desirable management practices will be outlined.

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Abstract

Vehicles which operate in agricultural row crops, need to strictly follow the established wheel tracks. Errors in navigation where the robot sways of its path with one or more wheels may damage the crop plants. The specific focus of this paper is on an agricultural robot operation in row cultures. The robot performs machine vision detecting weeds within the crop rows and treats the weeds by high precision drop-on-demand application of herbicide. The navigation controller of the robot needs to follow the established wheel tracks and minimize the camera system offset from the seed row. The problem has been formulated as a Nonlinear Model Predictive Control (NMPC) problem with the objective of keeping the vision modules centered over the seed rows, and constraining the wheel motion to the defined Wheel tracks. The system and optimization problem has been implemented in Python using the Casadi framework. The implementation has been evaluated through simulations of the system, and compared with a PD controller. The NMPC approach display advantages and better performance when facing the path constraints of operating in row crops.

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Abstract

Vehicles which operate in agricultural row crops, need to strictly follow the established wheel tracks. Errors in navigation where the robot sways of its path with one or more wheels may damage the crop plants. The specific focus of this paper is on an agricultural robot operation in row cultures. The robot performs machine vision detecting weeds within the crop rows and treats the weeds by high precision drop-on-demand application of herbicide. The navigation controller of the robot needs to follow the established wheel tracks and minimize the camera system offset from the seed row. The problem has been formulated as a Nonlinear Model Predictive Control (NMPC) problem with the objective of keeping the vision modules centered over the seed rows, and constraining the wheel motion to the defined Wheel tracks. The system and optimization problem has been implemented in Python using the Casadi framework. The implementation has been evaluated through simulations of the system, and compared with a PD controller. The NMPC approach display advantages and better performance when facing the path constraints of operating in row crops.

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Abstract

The purpose of our study is to explore the possibility to use proximate RGB imagery as basis for site-specific management of perennial weeds in small grain cereals. The targeted species are the broadleaved weeds Cirsium arvense (L.) Scop. (Creeping thistle) and Sonchus arvensis L. (Perennial sowthistle) and the grass weed Elymus repens (L.) Gould. (Common quackgrass). These are the main challenges for perennial weed control in cereals in Norway and temperate zone. The overall idea is to make weed maps based on images acquired during harvest in autumn (August/September) and use these maps for site-specific weed management when these species are normally managed in Norway, i.e. 3-4 weeks after harvest (E. repens) or in the following spring, i.e. late May/early June (C. arvense and S. arvensis). An on-the-go weed detection and glyphosate application in one operation before harvest is also a possible usage of our image-based method where this timing of glyphosate application is allowed. Images were acquired with a consumer grade camera mounted on a 3 m pole and tilted to mimic images acquired from the roof of a combine harvester. Images were acquired few days before harvest, a period where the cereals are yellowish and weed leaves and stalks are still green. Plots, 8 m by 8 m, were established in cereals to cover a wide range in weed pressure- and flora. The four plot corners were marked with white styrofoam balls mounted on sticks prior imaging and recorded with GPS (10 cm accuracy). The machine vision algorithm performs first a geometrical transform to represent the images as pseudo-orthonormal to the ground plane. This transform is aided by white styrofoam balls marking the corners of the plot with known distance. In the intended practical use, the transform can be done by obtaining the camera-angle and heading from inertial and GPS measurements and assuming level ground. The classification algorithm starts by segmenting the image into a class for green parts of the weeds (leaf, stalk), and three classes for flower heads (yellow, white and purple), by using threshold filters in the HSV colour space. A connected components analysis is then performed on each of the binary images, where the very small regions are filtered out. The area and centre of each region is calculated for comparison with ground truth observations. Two types of ground truth data for evaluation of the algorithm are available: Manual assessment of weed coverage from computer display of images and weed maps based on GPS measurements at the time for their management. Machine vision algorithm outputs versus ground truth data will be presented.

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Abstract

The Asterix project develops an autonomous robot for automatic weed control in row-crops. The system will only apply a fraction of the herbicide used in conventional application. The Food and Agriculture Organization of the United Nations estimate that the worlds food production needs to increase by 60 % by to feed the growing world population. This cannot be ful lled by conventional agricultural methods on the worlds available farm lands. Precision Agriculture (PA) is the concept of measuring eld variability and introducing this information as a feed-back to the crop management. PA can increase yields and optimize the resource inputs, and reduce environmental damages by avoiding excess use of herbicides, pesticides and fertilizers. The Asterix project is an ultra-precise weed control approach, where individual weed leaves are controlled by herbicide droplets. The droplets are dispensed by an 18 cm wide array of drop-on-demand nozzles (DoD).

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Abstract

Arable weeds are generally distributed in patches, while herbicides are applied uniformly. Herbicides can be saved if only the patches are sprayed, i.e. patch spraying (PS). Bottlenecks for cost-effective PS are weed monitoring technology and valid technology-based decision rules for PS (thresholds). The novel machine vision algorithm Weedcer has been developed as an efficient weed monitoring tool for PS. Weedcer estimates the proportions of young weed leaves and cereal leaves in high resolution red–green–blue images. We conducted field trials to test relative weed cover (RWC) and relative mayweed cover (RMC) estimated by Weedcer as decision rules for PS. RWC is the total weed cover divided by the total plant cover and RMC is the mayweed cover divided by the total plant cover. The main criterion for evaluation and basis of these thresholds was the measured grain yield. Images (about 0.06-m2) were acquired with a GPS guided autonomous field robot in spring, the normal time for spraying seed-propagated broadleaf weeds in both winter – and spring cereals in Norway. Three map-based trials (weed monitoring and spraying in two separate operations) showed that mean RWC per management unit (12.0 × 12.5-m) was generally adequate. In winter wheat heavily infested with scentless mayweed (Tripleurospermum inodorum (L.) Sch.Bip.) and/or scented mayweed (Matricaria recutita L.), the mean RMC per management unit was more adequate. Progress during the project allowed three additional trials conducted in real-time (weed monitoring and spraying in the same operation). These were conducted with the robot in spring cereals, and showed that a weighted moving average of RWC per image was adequate. The sprayed and unsprayed management units in these trials were minimum 3.0 × 3.0-m and 0.5 × 3.0-m, respectively. Results indicated that the Weedcer-based thresholds should be lower in wheat (Triticum aestivum) than in barley (Hordeum vulgare).

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Abstract

Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm ("WeedFinder") estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red-green-blue images. The ability of "WeedFinder" to predict 'spray'/'no spray' decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, "WeedFinder" gave correct spray decisions in 65-85% of the test images. With discriminant analysis, corresponding mean rates were 84-90%. Future versions of "WeedFinder" must be more accurate and accommodate weed species recognition.

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Abstract

A possible cost-effective real-time patch spraying implementation against seed-propagated broad-leaved weeds in cereals is a camera mounted in front of the tractor taking images at feasible distances in the direction of travel, on-board image analysis software and entire boom switched on and off. To assess this implementation, manual weed counts (0.25 m(2) quadrats) in a 1.5 m x 2 m grid, were used to simulate camera outputs. Each quadrat was classified into 'spray' and 'not spray' decisions based on a threshold model, and the resulting map defined the 'ground truth'. Subsequently, 'on/off' spraying at larger control areas where sizes were given by the boom width and image distance, and spraying decision controlled by weed status at the single quadrat simulating the camera's view, were simulated. These coarser maps were compared with 'ground truth', to estimate mapping error (area above threshold not sprayed), spraying error (area below threshold sprayed), total error (sum of mapping and spraying error) and the herbicide reduction. Three levels of the threshold model were tested. Results were used to fit models that predict errors from boom width and image distance. Size of control area did not on average affect the magnitude of the simulated herbicide reductions, but the bigger the control area the higher the risk that the simulated herbicide reduction deviate from the reduction in 'ground truth'. Mean simulated herbicide reductions were 42-59%, depending on threshold level. Only minor differences due to threshold level were seen for mean mapping and spraying errors at given spraying resolutions. Using original threshold level and image distance 2 m, predicted total errors for boom widths 2 m, 6 m, 20 m and 40 m would be 6%, 10%, 16% and 17%, respectively. Results indicate that control area should not exceed about 10 m 2 if acceptable total error is maximum 10%.