Title: ACTUAL STUDY OF PLANT DISEASE DISCOVERY WITH NEURAL NETWORK TO CHECK SOCIAL & ECONOMIC DAMAGE
Abstract: Plant diseases pose significant threats to global food security, agricultural sustainability, and economic stability.Timely detection and accurate diagnosis of these diseases are crucial for effective mitigation strategies.This study explores the integration of neural network technology to enhance the process of plant disease identification and evaluates its broader impact on social and economic aspects.The research employs a dataset comprising diverse plant species, encompassing a wide range of diseases, collected from various geographic locations.Convolutional Neural Networks (CNNs) are utilized to develop a robust and efficient model for the automated identification of plant diseases based on leaf images.The neural network is trained on a large dataset to ensure generalizability and adaptability to different environmental conditions and plant varieties.The study not only focuses on the technological aspect of disease detection but also extends its analysis to the social and economic dimensions.Social impacts are assessed by investigating the consequences of plant diseases on local communities, farmers, and labor markets.Economic impacts are evaluated by quantifying yield losses, market disruptions, and associated financial repercussions.To validate the effectiveness of the neural network model, field experiments are conducted in collaboration with agricultural communities.Realworld scenarios are simulated to assess the model's accuracy, sensitivity, and specificity in detecting plant diseases under diverse conditions.The findings of this research contribute to the development of a holistic framework for plant disease management.The integration of advanced technologies, such as neural networks, not only enhances the speed and accuracy of disease identification but also enables proactive decision-making in agriculture.The study sheds light on the interconnectedness of plant diseases with social and economic factors, providing valuable insights for policymakers, agricultural practitioners, and researchers.