Krzysztof Kusnierek
Forsker
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Today’s modern precision agriculture applications have a huge demand for data with high spatial and temporal resolution. This leads to the need of unmanned aerial vehicles (UAV) as sensor platforms providing both, easy use and a high area coverage. This study shows the successful development of a prototype hybrid UAV for practical applications in precision agriculture. The UAV consists of an off-the-shelf fixed-wing fuselage, which has been enhanced with multi-rotor functionality. It was programmed to perform pre-defined waypoint missions completely autonomously, including vertical take-off, horizontal flight, and vertical landing. The UAV was tested for its return-to-home (RTH) accuracy, power consumption and general flight performance at different wind speeds. The RTH accuracy was 43.7 cm in average, with a root-mean-square error of 39.9 cm. The power consumption raised with an increase in wind speed. An extrapolation of the analysed power consumption to conditions without wind resulted in an estimated 40 km travel range, when we assumed a 25 % safety margin of remaining battery capacity. This translates to a maximal area coverage of 300 ha for a scenario with 18 m/s airspeed, 50 minutes flight time, 120 m AGL altitude, and a desired 70 % of image side-lap and 85 % forward-lap. The ground sample distance with an in-built RGB camera was 3.5 cm, which we consider sufficient for farm-scale mapping missions for most precision agriculture applications.
Sammendrag
Currently, sugar snap peas are harvested manually. In high-cost countries like Norway, such a labour-intensive practise implies particularly large costs for the farmer. Hence, automated alternatives are highly sought after. This project explored a concept for robotic autonomous identification and tracking of sugar snap pea pods. The approach was based on a combination of visible–near infrared reflection measurements and image analysis, along with visual servoing. A proof-of-concept harvesting platform was implemented by mounting a robotic arm with hand-mounted sensors on a mobile unit. The platform was tested under plastic greenhouse conditions on potted plants of the sugar snap pea variety Cascadia using LED-lights and a partial shade. The results showed that it was feasible to differentiate the pods from the surrounding foliage using the light reflection at the spectral range around 970 nm combined with elementary image segmentation and shape modelling methods. The proof-of-concept harvesting platform was tested on 48 representative agricultural environments comprising dense canopy, varying pod sizes, partial occlusions and different working distances. A set of 104 images were analysed during the teleoperation experiment. The true positive detection rate was 93 and 87% for images acquired at long distances and at close distances, respectively. The robot arm achieved a success rate of 54% for autonomous visual servoing to a pre-grasp pose around targeted pods on 22 untouched scenarios. This study shows the potential of developing a prototype robot for semi-automated sugar snap pea harvesting.
Forfattere
Krzysztof KusnierekSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
It has been long known that thermal imaging may be used to detect stress (e.g. water and nutrient deficiency) in growing crops. Developments in microbolometer thermal cameras, such as the introduction of imaging arrays that may operate without costly active temperature stabilization, have vitalized the interest in thermal imaging for crop measurements. In this study, we have focused on the challenges occurring when temperature stabilization is omitted, including the effects of focal-plane-array (FPA) temperature, camera settings and the environment in which the measurements are performed. Further, we have designed and tested models for providing thermal response from an analog LWIR video signal (typical output from low-cost microbolometer thermal cameras). Finally, we have illustrated and discussed challenges which typically occur under practical use of thermal imaging of crops, by means of three cereal showcases, including proximal and remotely based (UAV) data acquisition. The results showed that changing FPA temperature greatly affected the measurements, and that wind and irradiance also appeared to affect the temperature dynamics considerably. Further, we found that adequate settings of camera gain and offset were crucial for obtaining a reliable result. The model which was considered best in terms of transforming video signals into thermal response data included information on camera FPA temperature, and was based on a priori calibrations using a black-body radiation source under controlled conditions. Very good calibration (r2>0.99, RMSE=0.32°C, n=96) was obtained for a target temperature range of 15-35°C, covering typical daytime crop temperatures in the growing season. However, the three showcases illustrated, that under practical conditions, more factors than FPA temperature may need to be corrected for. In conclusion, this study shows that thermal data acquisition by means of an analog, uncooled thermal camera may represent a possible, cost-efficient method for the detection of crop stress, but appropriate corrections of disturbing factors are required in order to obtain sufficient accuracy.
Divisjon for matproduksjon og samfunn
SolarFarm
SolarFarm - En systemstudie av hvordan solstrøm produsert på gårdsnivå kan drive elektriske og delvis selvstyrte farkoster i et presisjonsjordbruk med reduserte utslipp av klimagasser
Divisjon for matproduksjon og samfunn
SolarFarm
SolarFarm - Exploring solar on-farm energy production combined with a fleet of electrical vehicles and precision agriculture for reduced GHG-emissions
Innovative løsninger for økt lønnsomhet i grøntnæringa - TEKNOBÆR
De to største utfordringene norsk grøntnæring står ovenfor, er høye arbeidskostnader og stabilt høye avlinger av god kvalitet. Det skjer en rask teknologisk utvikling i og rundt landbruket. Bruk av avansert teknologi, inkludert robotisering til for eksempel behovsprøvd gjødsling, vanning og plantevern og ved innhøsting er ikke lengre framtiden, det er på full fart inn i internasjonalt landbruk. Hagebruksnæringen har arbeidsintensive produksjoner som har sterkt behov for å finne innovative løsninger på de høye arbeidskostnadene. Mer ekstremvær og utfordringer med resistens hos plantepatogene sopper og skadedyr mot kjemiske plantevernmidler gjør at norsk grøntproduksjon i framtida vil foregå med vær-vern, ved dyrking under et dekke av plast. I land som England, Frankrike, Nederland og Spania foregår i dag mer enn 90% av bærproduksjonen i plasttunneler. Bruk av plasttunneler gir bedre plantevekst, høyere avlinger og mindre problemer med viktige skadegjørere. Avlingen per arealenhet kan økes med 50 til 100%. Men det er mange utfordringer som må løses: næringsforsyningen må bli mer presis, plantevernstrategiene må tilpasses, kvaliteten på plantematerialet må forbedres, etc. I plasttunneler kan planter dyrkes i bakken eller på opphøyde renner, i jord eller i substrat. Plantene kan ‘designes’ til produksjonssystemet og overvintres på kjølelager, slik at produksjonen kan programmeres og sesongen utvides. En slik semi-kontrollert produksjon vil gi flere muligheter til å effektivisere arbeidsoperasjoner og gjøre produksjonen mer miljøvennlig. Satsingen vil gi økt kunnskap/kompetanse i NIBIO og bidra til å gjøre instituttet nasjonalt ledende innen forskning på tekniske løsninger i landbruket.
SINOGRAIN II
Scientists from NIBIO and CAAS are working together using innovative technologies in order to improve productivity, food safety and sustainability in Chinese agriculture.