IntelliCrop: Optimizing Crop Production with Smart Agriculture

Although agriculture is the backbone of the Indian economy, it faces numerous challenges to its sustainability, including soil degradation, pollution, and the impact of pollution from corporate enterprises, which poses a significant threat. To address these challenges, predictive tools that can evaluate crop suitability to varying soil conditions and nutrient compositions are needed; however, the application of data mining and machine learning in agriculture is still relatively new. This study attempts to close this gap by utilizing a variety of regression techniques, such as random forest, linear regression, decision tree regression, polynomial regression, and support vector regression. These methodologies are used to predict crop performance. The study also explores the important topic of plant diseases, which are a major threat to crop quality, productivity, and economic viability. Advanced methods for monitoring pests are explained, including thorough methods for identifying plant pests in important crops including cotton, citrus, and rice. These surveillance systems reduce labour-intensive tasks and mitigate human error by enabling automated monitoring across large agricultural fields. This paper presents a comprehensive strategy for improving agricultural sustainability in India by combining cutting-edge pest surveillance systems with data-driven predictive models. The suggested approaches build resilience against environmental issues and advance long-term agricultural prosperity by offering farmers, policymakers, and other agricultural stakeholders practical insights.