Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset.

• An automatic image segmentation algorithm is proposed to remove the background information of the image and improve the disease recognition accuracy in practical applications. • A more representative dataset of crop disease leaves is constructed to train and test the deep learning model. • A cash crop disease identification system on mobile devices is designed with the correct recognition rate of more than 80% for 27 diseases of 6 crops. Using deep learning methods to identify cash crop diseases has become a current hotspot in the field of plant disease identification. However, recent studies have demonstrated that the complex background information of crop images from practical application and insufficient training data can cause the wrong recognition of deep learning. To address this problem, in this paper we present an identification method of cash crop diseases using automatic image segmentation and deep learning with expanded dataset. An Automatic Image Segmentation Algorithm(AISA) based on the GrabCut algorithm is designed to remove the background information of images automatically while retaining the disease spots. It doesn't need to select the object manually during image processing and is of much lower time cost compared with the GrabCut algorithm. The MobileNet Convolutional Neural Network(CNN) model is selected as the deep learning model and plenty of crop images from the Internet and practical planting bases are added to expand the public dataset PlantVillage for the purpose of improving the generalization ability of MobileNet. The images are processed by the AISA before they can be used for extracting disease features, which reduces calculations significantly and ensures that the disease features of the crop leaf can be extracted accurately. Moreover, we design a cash crop disease identification system for mobile smart devices. The experimental results show that the system has a correct recognition rate of more than 80% for the 27 diseases of 6 crops described in this paper and then has a high value of practical application. [ABSTRACT FROM AUTHOR]