Innovative Approaches to Early Disease Detection in Medicinal Plants Using Deep Learning Techniques

Abstract

Early identification of diseases in medicinal plants is also critical for maintaining the quality and efficacy of drugs, and in the case of traditional medicine, this is even more important. With such a perspective, the study discusses how deep learning techniques are used in the detection of diseases in key medicinal plants like turmeric, aloe vera, ashwagandha, and Terminalia arjuna. The focus is on incorporating thermal imaging to enhance the accuracy of the detection process. It includes studies concerning the capability of these systems: CNNs and hybrid algorithms in distinguishing between diseased and healthy plants. The paper highlights state-of-the-art techniques for the detection, advanced thermal imaging and federated learning in developing new, sustainable herb cultivation and disease management practices. By findings, the identified models like hybrid CNN-SVM and YOLOV3-Tiny established the identification of plant diseases with accuracy that runs from 94 to 97%. Hence, there is a great potential for increasing medicinal herbs yield and quality.