• Somnath M Melasagare
• Bhagyalaxmi Shashikant Kadam
• Shruti Dnyaneshwar Paradkar
• Karuna Nitin Shetye

Artificial Intelligence (AI) plays a significant role in agriculture helping the agriculture sector to enhance productivity and reduce costs while dealing with food security issues, climate change, resource management, environment management, and much more. The traditional methods used by the farmers were not sufficient enough to fulfill these requirements. These conventional methods are labor-intensive and time-consuming and often fail to provide accurate, real-time insights. To address these issues, these AI-based crop monitoring systems have emerged as transformative tools for modern agriculture, offering data-driven solutions for precision farming, yield optimization, and resource management. Crop monitoring includes yield prediction, crop recommendation, and plant disease detection. The models are trained with image and numerical datasets. A website is developed to monitor crops and provide solutions. The optimal crop can be suggested based on surrounding conditions by analyzing important variables Rainfall, Average rainfall, and temperature using various models namely random forest, and convolutional neural networks. Random forest can be used for crop yield prediction which gives Random Forest Regressor, an ensemble learning method that combines multiple decision trees to improve prediction accuracy and reduce overfitting and recommendation plant diseases that can be detected using a convolutional neural network. The purpose of this project is to help farmers choose suitable crops, and differentiate crops from disease detection. It enables improved yield and productivity, and increased profitability.

crop monitoring, machine learning, deep learning, yield prediction, crop disease detection, random forest, CNN

"AI BASED CROP MONITORING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.i650-i656, March-2025, Available :http://www.jetir.org/papers/JETIR2503887.pdf

"AI BASED CROP MONITORING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppi650-i656, March-2025, Available at : http://www.jetir.org/papers/JETIR2503887.pdf