Enhancing the Early Detection and Diagnosis of Plant Diseases Using Deep Learning and Advanced Imaging Techniques

Abstract

Feeding a growing global population and meeting nutritional needs requires farming. It provides food and clothing reliably. It boosts the economy and employs many, especially in distant areas. Early plant disease diagnosis is crucial to agricultural system sustainability. It provides farmers with the knowledge they require to make informed decisions, which helps to reduce crop losses and contributes to an agriculture industry that is more efficient and sustainable. Many methods have been developed recently to identify and categorize plant diseases at an earlier stage, however it is essential that these methods be as precise and reliable as possible. Deep learning is one of the greatest methods for early detection of plant diseases. The purpose of this study piece is to suggest a revolutionary design that is referred to as the Ensemble ResNet LSTM for Horticulture plants (ERLSTMH). The LSTM network and dropout layer are both incorporated into the ResNet-50 architecture through the process of this suggested design. In order to do an analysis of calculated metrics, such as precision, recall, F1-score, sensitivity, and specificity, the proposed design is utilized. This architecture has provided the maximum level of accuracy. In contrast to all of the earlier structures, the architecture that was recommended was able to attain an exceptionally high level of accuracy, which was 97.147% effective.