Empowering Artificial Intelligence Techniques with Soft Computing of Neutrosophic Theory in Mystery Circumstances for Plant Diseases

Abstract

Plant diseases are one of the factors that leadto yield and economic losses, which have a direct effect on national and international food production systems. One of the most essential ways to avoid agricultural product loss or reduction in amount is to diagnose plant diseases promptly and accurately. Hence, the diagnosis process for plants is crucial and should be conducted accurately. Moreover, this study focuses on this process by constructing an Artificiality Diagnostics Framework (ADF) to serve the study’s objectives which entailed conducting diagnosis for plants in a professional and precise mannerover uncertain environments. Thus, neutrosophic theory is considered the principal ingredient in our ADF. Due to the ability of neutrosophic to divide imagesinto Truth (T), Falsity (F), and Indeterminacy (I). Also, deep learning (DL) is considered another principal ingredientin treating vast samples of datasets. Our comparative analysis of the leavesof potatoesis conducted whether leveraging neutrosophic and without utilizing Neutrosophic. ResNet50, ResNet152, and MobileNetare the principal ingredients for the training dataset. The findings of implementing these networks indicated that ResNet50 achieved the highest accuracy of 0.915 in the T domain, ResNet152 achieved the highest accuracy of 0.905 in the True (T) domain, and MobileNet achieved the highest accuracy of 0.915 in Truth (T) domain. Accuracy of 0.863 in Indeterminate (I).

Keywords

• neutrosophic theory
• deep learning
• plant disease