• G.Harshavardhan
• Cheemalapati jithin
• Bolisetty Sarath Sai Manikanta
• Asinaparthi Mani Kumari
• Gudimetla Gayatri

Agriculture is the backbone of many countries, including India, and provides livelihoods to millions of people facing challenges such as climate change and plant disease outbreaks. Through research, a web application has been developed that provides real-time recommendations for crop selection based on various factors such as soil nutrients, temperature, humidity, pH levels, and rainfall. Recent advances in machine learning and artificial intelligence offer promising solutions to these problems, enabling accurate, data-driven decision-making in agriculture. These technologies have the potential to transform how we predict crop yields and detect plant diseases, thus improving agricultural practices. To achieve this, we trained and examined seven machine learning models: Decision Tree, Naive Bayes, SVM, Logistic Regression, Random Forest, XGBoost, and KNN. Among these, Random Forest gives the highest accuracy, making it the best choice for crop forecasting. In addition to crop recommendation, the web application also integrates a Plant Disease Identification system using Convolutional Neural Networks (CNN). These two models are integrated into the Smart Crop Recommendation System with Plant Disease Identification. This system provides farmers with comprehensive support, offering both crop recommendations based on various factors and precise identification of crop diseases through image analysis. By combining these models, the system enables farmers to make informed decisions, optimize crop selection, and effectively manage plant diseases for sustainable agriculture and enhanced productivity.

Machine Learning, Crop Prediction, Agricultural Environments, Random Forest, Gradient Boosting, Deep Neural Networks, Feature Engineering, Climate Change, Food Security, Real-Time Data, Soil Properties, Yield Optimization, Engineering , Real-Time Processing,Machine Learning, Neural Networks,fertilizer recommendation, plant disease identification.

"Harnessing Machine Learning for Dynamic Crop Prediction in Diverse Agricultural Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g188-g193, March-2025, Available :http://www.jetir.org/papers/JETIR2503623.pdf

"Harnessing Machine Learning for Dynamic Crop Prediction in Diverse Agricultural Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg188-g193, March-2025, Available at : http://www.jetir.org/papers/JETIR2503623.pdf