Crop Disease Prediction Using Deep Learning Algorithms

Abstract

Good crops yield good food which in turn nourishes the human body and mind. But these crops across the globe face the threat of various diseases that remain unidentified, leading to poorer quality and quantity of crops. But, in recent times, the increasing adoption of smartphones worldwide and current developments in image processing of computers enabled by deep models of learning have made smartphone-based disease detection possible. In this chapter, the authors train a deep convolutional neural network (CNN) model to recognize 18 crop species and 28 diseases by feeding it a pre-available dataset of 70,296 photographs of unhealthy and healthy crop leaves taken under control. On a sustained test set, the trained model shows up to 99% accuracy, proving the feasibility of the method.

AI can empower agriculture for global food security: challenges and prospects in developing nations

“…Equally, the use of ResNet-50 with an accuracy of 95.61% in plant disease and pest detection has also been reported ( Fang et al, 2020 ). Numerous techniques exist based on AI techniques for plant disease and pest identification ( Pandey et al, 2022 ; Zheng et al, 2022 ; Singh et al, 2023 ) ( Nagasubramanian et al, 2019 ; Thenmozhi and Reddy, 2019 ; Liu and Wang, 2021 ; Xu et al, 2022 ).…”