Semantic segmentation: A modern approach for identifying soil clods in precision farming

Abstract

Clod identification and determination of aggregate size distribution have undeniable importance in tillage operations, it affects other missions in the field regarding the energy issue. The objective of the study is accurate identification of clods based on a state-of-the-art method entitled semantic segmentation. In this regard, a deep network in the domain of deep learning was used. As the shape of clods on the surface of the ground is often irregular, object detection algorithms are unable to determine the boundary and, they only put a bounding box around the objects. Thus, there is a gap in this regard with deep learning based semantic segmentation filling this gap properly. In order to identify soil clods and compute their various geometrical specifications in different sizes and then to deal with the clods according to required action in variable-rate applicators, VGG16, which is a deep model, was used for implementing the semantic segmentation task. RGB images obtained from a stereo camera were used for feeding the proposed model as stereo cameras are relatively robust to the ambient light conditions and provide high resolution data in real field conditions. The pixels in each image of the dataset were divided into five groups of clod size, based on the equivalent diameter of each clod. These image pixels were labeled for training the network and extracting the required features. Finally, different soil clods were segmented and classified. A classical watershed segmentation was applied and the deep model was also trained in binary setting to evaluate the performance of the deep model and the results of the binary segmentations were compared. The mean accuracy and mean intersection over union (IoU) of the binary semantic segmentation reached 89.09% and 80.50%, respectively. Also, the watershed segmentation yielded 85.17% and 72.01%, for the same metrics. These results indicated that the semantic segmentation used in this study has significant advantages over the conventional watershed segmentation method.