Integrating First-Order-Logic with Artificial Intelligence: A Novel Framework for the Development of Smart Based Ginger Plants’ Disease Detection and Decision Support System

Abstract

The conventional methods of visually inspecting ginger rhizome against diseases such as fungi by experts or farm-ers at early stage is not effective due to human errors, fatigue, costs, and time consuming. In addition, combined disease management strategy such as soil treatment approach, biological control proxies, and application of re-sistant varieties are most often employed for effective monitoring of rhizome rot but not without issues of robust effectiveness. In view of this development, innovative technologies such as artificial intelligence, machine learning, and Internet of Things have been employed towards viable solutions. Based on literature, this effort is promising but not without a gap that necessitated further research. Therefore, this research aims to design a smart based frame-work for developing ginger plants’ disease detection and decision support model. At the stage of developing the framework, images data will be collected using either smartphones, drones, or a positioned camera. CNN, ensemble learning, LSTM techniques will be used as training model pipeline. Furthermore, as part of the innovative strategy into this research, a novel decision support algorithm is designed. This will inadvertently reduce poverty, enhances sustainable agriculture, and ensure sustainable production patterns when fully implemented, which are in line with the sustainable development goals 1, 2, and 12.