“Advanced Deep Learning Techniques for Plant Disease Diagnosis and Treatment”

Abstract

Abstract: The pivotal role of agriculture in shaping people’s lives and economies is evident, with a substantial contribution to GSP and employment. The prevalence of diseases poses a major challenge. Novel system is introduced for the early detection of leaf diseases using Deep Learning and Tensorflow technology. The model analyses pictures from plant village, focusing on bell pepper, tomato and potato plants. Climate change soil erosion and difficulty in detecting pathogens underscore the need for early disease recognition to minimize pesticide use. The system demonstrates a remarkable 95.80% correctness in disease identification, benefiting cultivators by recommending appropriate measures in a separate context. The recognition of medicinal plant gains importance for various stakeholders. A fully automated approach combining computer vision and machine learning achieves an accuracy of 90.1% in detecting 24 different medicinal plant species, outperforming other methods.