Smart Cyber-Physical System-Based Plant Disease Detection for Agriculture

Abstract

Agriculture cyber-physical systems are becoming increasingly significant in improving crop quality and output while employing the least amount of farmland possible. Many agricultural components and methods have been automated to produce faster and superior-quality items. As a result, several methods and techniques to help prevent or reduce plant diseases have been created. Imaging analysis tools and gas sensors are increasingly being included in innovative cyber agribusiness for plant disease monitoring. This chapter develops intelligent cyber-physical system-based plant disease detection for successfully preventing and managing plant diseases and decision-making. To extract the important features linked to the sick region, a novel multi-statistical feature extraction technique has been developed. The proposed methodology is implemented using a Raspberry Pi board running Stretch OS. Metrics like detection and classification accuracy are used to assess the efficacy of the multi-statistical feature extraction methodology on the plant disease detection system.