Deep Learning-Based Plant Stress Diagnosis: An Optimized Generative Augmentation Model Approach

Agriculture influences economic stability and prosperity, and it is an important part of the intricate tapestry of our country’s economy. However, agricultural business has a number of barriers, notably changing weather conditions that induce a variety of serious diseases in plants, depressing crop yields, and revenues. An optimized Convolution Neural Network model for identifying and reliably detecting plant stress phenotyping. By using the capabilities of these sophisticated neural networks, we were able to successfully raise the stress detection process beyond the boundaries of traditional approaches. Plant stress phenotypic data from important crop species, including wheat, rice, cotton, and maize, are utilized to systematically assess the model’s efficacy. The proposed model, with its exceptional accuracy of 96.67%, demonstrates its potential to become one of the world’s leading agricultural decision-support systems, benefiting a wide range of businesses. This work, which establishes a new standard for harnessing artificial intelligence for sustainable agriculture practices, will assist plant stress detection and improved crop management.