An Improved Convolutional Neural Network (CNN) for Disease Detection and Diagnosis for Multi-crop Plants

Agriculture is one of the largest sectors that contribute to the economic growth of countries, including Malaysia. However, plant diseases affect the quality of the harvest and impede farmers’ maximum yield output. Therefore, early detection of diseases in plants is vital to curb infection, reduce food waste, and reduce their carbon footprint. However, many detection methods are complex, require high computational power and time to perform the required analysis and focus only on a particular species or strain of the disease. These requirements would likely deter most users in remote areas or poorer economic states. This paper proposes a convolutional neural network to determine multi-class plant diseases that is memory efficient, has a small trainable parameter number, and is compact enough to work even on mobile devices. The plant images were pre-processed to ensure that they were validated accurately and to minimise overfitting. Then, the proposed convolutional neural network was trained using a publicly available dataset consisting of 54306 images, followed by validation and testing. Finally, the completed model is saved, and the data obtained is transferred to a cloud network using wireless sensor networks. The proposed method obtained 96.87% accuracy with 100 epoch training iterations, rivalling famous architectures such as VGG16 and MobileNetV2. The experimental results demonstrate the feasibility and robustness of the method for disease detection in multi-crop plants.