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Weed and crop segmentation is becoming an increasingly integral part ofprecision farming that leverages the current computer vision and deep learningtechnologies. Research has been extensively carried out based on imagescaptured with a camera from various platforms. Unmanned aerial vehicles (UAVs)and ground-based vehicles including agricultural robots are the two popularplatforms for data collection in fields. They all contribute to site-specificweed management (SSWM) to maintain crop yield. Currently, the data from thesetwo platforms is processed separately, though sharing the same semantic objects(weed and crop). In our paper, we have developed a deep convolutional networkthat enables to predict both field and aerial images from UAVs for weedsegmentation and mapping with only field images provided in the training phase.The network learning process is visualized by feature maps at shallow and deeplayers. The results show that the mean intersection of union (IOU) values ofthe segmentation for the crop (maize), weeds, and soil background in thedeveloped model for the field dataset are 0.744, 0.577, 0.979, respectively,and the performance of aerial images from an UAV with the same model, the IOUvalues of the segmentation for the crop (maize), weeds and soil background are0.596, 0.407, and 0.875, respectively. To estimate the effect on the use ofplant protection agents, we quantify the relationship between herbicidespraying saving rate and grid size (spraying resolution) based on the predictedweed map. The spraying saving rate is up to 90% when the spraying resolution isat 1.78 x 1.78 cm2. The study shows that the developed deep convolutionalneural network could be used to classify weeds from both field and aerialimages and delivers satisfactory results.

Transferring learned patterns from ground-based field imagery to predict  UAV-based imagery for crop and weed semantic segmentation in precision crop  farming