Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2024
Authors
Lingwei Dong Yuxin Miao Xinbing Wang Krzysztof Kusnierek Hainie Zha Min Pan William D. BatchelorAbstract
Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.
Abstract
Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.
Abstract
Introduction: Production of strawberries in greenhouses and polytunnels is gaining popularity worldwide. This study investigated the effect of reuse of coir and peat, two substrates commonly adapted to soilless strawberry production, as well as stand-alone wood fiber from Norway spruce, a promising substrate candidate. Methods: The experiment was performed in a polytunnel at NIBIO Apelsvoll, Norway, and evaluated both virgin substrates, as well as spent materials that were used in one or two years. Yield, berry quality and plant architecture of the strawberry cultivar ‘Malling Centenary’ were registered. In addition, chemical and physical properties of virgin and reused substrates were investigated. Results: While plants grown in peat and wood fiber had highest yield in the first year of production, the berry yield was slightly reduced when these substrates were utilized for the second and third time. However, yield was comparable to the yield level attained in new and reused coir. Interestingly, berries grown in wood fiber had a tendency to a higher sugar accumulation. This substrate also produced the highest plants. Stand-alone wood fiber was the substrate with the highest accumulation of nitrogen during the three consecutive production cycles. All three investigated materials revealed a trend for decreased potassium accumulation. Wood fiber is characterized by the highest percentage of cellulose, however after three years of production the cellulose content was reducedto the same levels as for coir and peat. Discussion: Implementation of wood fiber as a growing medium, as well as general practice of substrate reuse can be therefore an achievable strategy for more sustainable berry production.
Abstract
This study investigated the effects of substrates composed of various ratios of wood fiber and peat (0, 25, 50, 75, and 100% peat (v/v)) mixed with different amounts of lime (0, 2, 4, 6, and 8 g L−1) and start fertilizer (0, 2, and 4 g L−1 Multimix) on the growth and biomass accumulation of petunia (Petunia x hybrida Vilm ‘Finity F1 Purple’) and basil (Ocimum basilicum L. ‘Marian’) in an ebb-and-flow greenhouse system. Growth parameters included plant height, weight, canopy diameter, and chlorosis symptoms for petunia, along with substrate pH and EC measurements. Petunia showed optimal growth in substrates with higher peat content, while basil produced satisfactory biomass across a pH range of 5–7 regardless of substrate type. Optimal petunia cultivation in 100% wood fiber required a significant dose of start fertilizer without lime. Monitoring pH and EC using pour-through and press methods revealed a pH decrease in substrates with added start fertilizer, while substrates with higher wood fiber content were less acidic. Substrates with over 50% (v/v) wood fiber without lime showed a rapid pH increase over five weeks. The pour-through method generally underestimated EC values compared to the press method. These findings contribute to optimizing the wood fiber/peat blends for sustainable horticulture.
Abstract
No abstract has been registered
Authors
Ingeborg Klingen Nils Bjugstad Therese With Berge Krzysztof Kusnierek Hans Wilhelm Wedel-Jarlsberg Roger Holten Anette Sundbye Lene Sigsgaard Håvard Eikemo Kirsten Tørresen Valborg KvakkestadAbstract
Droner til bruk i plantevern i jord- og hagebruk er relativt nytt og i dette forprosjektet ønsket vi å etablere et kunnskapsgrunnlag for bærekraftig bruk av droner i norsk plantevern. Vi gjorde dette ved å: 1) Systematisere kunnskap om avdrift fra plantevernmidler fra sprøytedroner, 2) Gjennomføre et pilotstudie på en metode for å måle avdrift og avsetning av plantevernmidler utenfor målområdet fra sprøyte droner, 3) Skaffe kunnskap om eksponering av dronepilot for plantevernmidler, 4) Skaffe kunnskap om miljøeksponering inkludert rester av plantevernmidler i drone-sprøytede plantekulturer, 5) Skaffe kunnskap om bruk av droner i presis påføring av plantevernmidler, lavrisikostoffer og biologiske kontrollorganismer, 6) Øke vår kunnskap om forskrifter og standarder som kan påvirke bruken av droner i integrert plantevern i Norge. Basert på kunnskap gjort tilgjengelig i dette forprosjektet, foreslår vi videre studier som er nødvendig å utføre for å kunne bruke droner i integrert plantevern på en smart måte. Vårt håp er at resultatene fra dette forprosjektet vil gjøre det mulig å ta beslutninger om hvordan droner bør brukes i plantevern i Norge for å være i tråd med direktivet for bærekraftig bruk av plantevernmidler (Direktiv 2009/128/EF). Det er spesielt målgrupper som bønder, landbruksrådgivningstjenester, agroindustri, forskere, nasjonale statlige organer som Mattilsynet og lovgivere som kan tenkes å ha nytte av å lese denne rapporten.
2023
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
Soil management is important for sustainable agriculture, playing a vital role in food production and maintaining ecological functions in the agroecosystem. Effective soil management depends on highly accurate soil property estimation. Machine learning (ML) is an effective tool for data mining, selection of key soil properties, modeling the non-linear relationship between different soil properties. Through coupling with spectral imaging, ML algorithms have been extensively used to estimate physical, chemical, and biological properties quickly and accurately for more effective soil management. Most of the soil properties are estimated by either near infrared (NIR), Vis-NIR, or mid-infrared (MIR) in combination with different ML algorithms. Spectroscopy is widely used in estimation of chemical properties of soil samples. Spectral imaging from both UAV and satellite platforms should be taken to improve the spatial resolution of different soil properties. Spectral image super-resolution should be taken to generate spectral images in high spatial, spectral, and temporal resolutions; more advanced algorithms, especially deep learning (DL) should be taken for soil properties’ estimation based on the generated ‘super’ images. Using hyperspectral modeling, soil water content, soil organic matter, total N, total K, total P, clay and sand were found to be successfully predicted. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties. An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for water, total organic C, extractable phosphorus, and total N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. More precise and detailed soil property estimation will facilitate future soil management.
Authors
Jiangsan ZhaoAbstract
No abstract has been registered
Abstract
The ageing population, climate change, and labour shortages in the agricultural sector are driving the need to reevaluate current farming practices. To address these challenges, the deployment of robot systems can help reduce environmental footprints and increase productivity. However, convincing farmers to adopt new technologies poses difficulties, considering economic viability and ease of use. In this paper, we introduce a management system based on the Robot Operating System (ROS) that integrates heterogeneous vehicles (conventional tractors and mobile robots). The goal of the proposed work is to ease the adoption of mobile robots in an agricultural context by providing to the farmer the initial tools needed to include them alongside the conventional machinery. We provide a comprehensive overview of the system’s architecture, the control laws implemented for fleet navigation within the field, the development of a user-friendly Graphical User Interface, and the charging infrastructure for the deployed vehicles. Additionally, field tests are conducted to demonstrate the effectiveness of the proposed framework.