Retracted: A Multiple Linear Regression Model for Crop Production using Machine Learning and Neural Network

Wireless sensors and artificial intelligence (AI)-based monitoring systems are in great demand and provide precise data extraction and analysis. Finding the best plant development parameters is the main goal of this paper. In this essay, the idea of lowering agricultural risks and fostering intelligent farming is presented. Agriculture has always advanced, however the AI-based sensor devices will set a new standard for smart agriculture. This research objective is to use machine learning methods using image processing to enhance the prediction state. The detection and management of cotton leaf disease detection is the paper's major goal, as stated above. This study includes numerous components, such as soil sensing, remote monitoring based on a server, moisture and temperature sensing, and detection of leaf disease. Plant illnesses that decrease yield if not treated in a timely manner are often brought on by insects and pathogens. In this study, a technique for maintaining soil quality and preventing diseases of cotton leaves is presented. To recognize and categorise leaf diseases, the recommended method employs a regression approach of artificial intelligence. The neural network using linear regression algorithm in the proposed model exhibits the highest level of efficiency in the identification and control of various illnesses by enhancing farmer-friendly farming practises.