Antje Giebel

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery

A crop height model (CHM) can be an important element of the decision making process in agriculture, because it relates well with many agronomic parameters, e.g., crop height, plant biomass or crop yield. Today, CHMs can be inexpensively obtained from overlapping imagery captured from unmanned aerial vehicle (UAV) platforms or from proximal sensors attached to ground-based vehicles used for regular management. Both approaches have their limitations and combining them with a data fusion may overcome some of these limitations. Therefore, the objective of this study was to investigate if regression kriging, as a geostatistical data fusion approach, can be used to improve the interpolation of ground-based ultrasonic measurements with UAV imagery as covariate. Regression kriging might be suitable because we have a sparse data set (ultrasound) and an exhaustive data set (UAV) and both data sets have favorable properties for geostatistical analysis. To confirm this, we conducted four missions in two different fields in total, where we collected UAV imagery and ultrasonic data alongside. From the overlapping UAV images, surface models and ortho-images were generated with photogrammetric processing. The maps generated by regression kriging were of much higher detail than the smooth maps generated by ordinary kriging, because regression kriging ensures that for each prediction point information from the UAV, imagery is given. The relationship with crop height, fresh biomass and, to a lesser extent, with crop yield, was stronger using CHMs generated by regression kriging than by ordinary kriging. The use of UAV data from the prior mission was also of benefit and could improve map accuracy and quality. Thus, regression kriging is a flexible approach for the integration of UAV imagery with ground-based sensor data, with benefits for precision agriculture-oriented farmers and agricultural service providers.

Discrimination of Common Ragweed (Ambrosia artemisiifolia) and Mugwort (Artemisia vulgaris) Based on Bag of Visual Words Model

Common ragweed is a plant species causing allergic and asthmatic symptoms in humans. To control its propagation, an early identification system is needed. However, due to its similar appearance with mugwort, proper differentiation between these two weed species is important. Therefore, we propose a method to discriminate common ragweed and mugwort leaves based on digital images using bag of visual words (BoVW). BoVW is an object-based image classification that has gained acceptance in many areas of science. We compared speeded-up robust features (SURF) and grid sampling for keypoint selection. The image vocabulary was built using K-means clustering. The image classifier was trained using support vector machines. To check the robustness of the classifier, specific model runs were conducted with and without damaged leaves in the trainings dataset. The results showed that the BoVW model allows the discrimination between common ragweed and mugwort leaves with high accuracy. Based on SURF keypoints with 50% of 788 images in total as training data, we achieved a 100% correct recognition of the two plant species. The grid sampling resulted in slightly less recognition accuracy (98 to 99%). In addition, the classification based on SURF was up to 31 times faster. Nomenclature: Common ragweed, Ambrosia artemisiifolia L.; mugwort, Artemisia vulgaris L.

Impact of sensor-controlled variable-rate fungicide application on yield, senescence and disease occurrence in winter wheat fields: Impact of sensor-controlled fungicide application on winter wheat

BACKGROUND Field experiments examining target-oriented variable-rate fungicide spraying were performed in 2015 and 2016. The spray volume was adapted in real time to the local green coverage level of winter wheat (Triticum aestivum L.), which was detected using a camera sensor. RESULTS Depending on the growth heterogeneity in the three strip trials in 2015, fungicide savings in the sensor-sprayed strip compared with the adjacent uniformly sprayed strip were 44%, 45% and 1%. In the 2016 field trial, the saving was 12%. There was no greater level of senescence or disease occurrence, and no higher yield losses in the camera-controlled variable-rate sprayed strips compared with the adjacent uniformly sprayed strips. CONCLUSIONS From an ecological and economical point of view, sensor-controlled variable-rate spraying technology, which uses the level of green crop coverage as the plant parameter to adapt the spray volume locally, can be an alternative to the common practice of uniform spraying.

Sensorgestützte Applikation von Pflanzenschutzmitteln

Ein bedarfsgerechter Einsatz von Pflanzenschutzmitteln fuhrt zur Kostenersparnis und vermindert den Eintrag von Bioziden in die Umwelt. Das bedeutet, vom ublichen flacheneinheitlichen Spritzen zu einer Applikation nur in den Bereichen uberzugehen, wo die jeweiligen Schaderreger wirtschaftliche Ertragsverluste verursachen, okonomische Schadensschwellen also uberschritten sind. Am ATB wurden zur technischen Umsetzung des teilflachenspezifischen Pflanzenschutzes Sensoren fur die Unkraut- sowie Pflanzenmasseerfassung entwickelt. In Verbindung mit einer Pflanzenschutzspritze werden diese zur Echtzeitapplikation von Herbiziden und Fungiziden eingesetzt. Neben dem Effekt der Einsparung von Betriebsmitteln ergaben zweijahrige Praxisversuche zur pflanzenbaulichen Bewertung des Verfahrens keine Ertragsverluste und kein starkeres Krankheits- oder Unkrautauftreten teilflachenspezifischer gegenuber flacheneinheitlicher Applikationen.