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.

中文翻译：

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语义分割：一种在精准农业中识别土块的现代方法

土块识别和骨料粒度分布的确定在耕作作业中具有不可否认的重要性，它会影响该领域的其他有关能源问题的任务。该研究的目标是基于名为语义分割的最新方法准确识别土块。在这方面，使用了深度学习领域的深度网络。由于地表土块的形状往往是不规则的，物体检测算法无法确定边界，只能在物体周围放置一个边界框。因此，基于深度学习的语义分割在这方面存在差距，适当地填补了这一差距。为了识别土块并计算不同大小的各种几何规格，然后根据可变速率施用器中所需的动作处理土块，使用深度模型 VGG16 来实现语义分割任务。从立体相机获得的 RGB 图像用于馈送所提出的模型，因为立体相机对环境光条件相对稳健，并在实际现场条件下提供高分辨率数据。根据每个土块的等效直径，将数据集中每个图像中的像素分为五组土块大小。这些图像像素被标记用于训练网络和提取所需的特征。最后，对不同的土块进行分割和分类。应用了经典的分水岭分割，并且还在二元设置中训练了深度模型，以评估深度模型的性能并比较二元分割的结果。二元语义分割的平均准确率和平均交集（IoU）分别达到了 89.09% 和 80.50%。此外，对于相同的指标，分水岭分割产生了 85.17% 和 72.01%。这些结果表明，本研究中使用的语义分割比传统的分水岭分割方法具有显着优势。对于相同的指标，分水岭分割产生了 85.17% 和 72.01%。这些结果表明，本研究中使用的语义分割比传统的分水岭分割方法具有显着优势。对于相同的指标，分水岭分割产生了 85.17% 和 72.01%。这些结果表明，本研究中使用的语义分割比传统的分水岭分割方法具有显着优势。