Furthermore, the model is examined with black rot, bacterial plaque, and rust diseases. Finally, the segmented leaves are input into the transfer learning model and trained by the dataset of diseased leaves under simple background. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan-Vese (CV) algorithm. Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. This paper proposes a mathematical model of plant disease detection and recognition based on deep learning, which improves accuracy, generality, and training efficiency. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, smart analyses, and planning.
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