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Path planning for optimized field-work pattern is an important task within precision farming. The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a new method for automated detection of the current cultivation direction of several thousands of agricultural fields and compared the current cultivation direction with an optimized cultivation direction generated from a path planning algorithm. Airborne imagery from 2019 was analyzed using a modified Gabor filter. The identification takes place on a sub-plot level and can therefore detect small-scale differences in cultivation direction within fields. The method for identification of current cultivation direction had a high success rate of 87.5%. Fields with a high potential to save turning maneuvers and to reduce the area of headland were identified. From 3410 fields, a total of 58162 turning maneuvers and 507 ha headland were saved. This corresponds to 14.1% of all turning maneuvers and 7.6% of the total headland area for all analyzed fields in Brandenburg. A high optimization potential was demonstrated for field paths when efficient processing directions are taken into account. The method can be extended to the analysis of satellite imagery and thus offers the possibility of identifying current cultivation directions with a high spatial and temporal resolution. In future, this knowledge can be embedded within decision support systems for real-time optimization of field machinery path planning to support sustainable cropping practices.

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.