• Omkar Ratnakar Gosavi
• SHWETA ANANT BACHUTE
• EKTA VISHNU BHOWAD
• KEDAR KESHAV SAWANT
• RAGINI BHOYAR

The requirement for sustainable practices to guarantee effective crop cultivation is posing an increasing challenge to the agriculture sector. To address this, this study presents a novel project aimed at putting into practice a cutting-edge crop recommendation system. Using real-time data on important environmental parameters like as temperature, moisture content of soil, weather, and contextual subtleties, the system utilizes machine learning models that have been trained beforehand to examine complex inputs. To ensure precision and accuracy, the training process makes use of modern technologies such as support vector machines (SVM), random forests, decision trees, and others. With the use of data-driven insights, the robust model that is produced offers farmers customized and optimal crop suggestions that will increase agricultural output and promote sustainable farming methods. This approach is a major step toward a new era of agricultural growth and wealth since it emphasizes the resilience and efficiency of the agricultural sector.

Data Collection, Feature Engineering, Algorithm Selection, Model Training, Validation, Recommendation Generation, User Interface.

"CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.n572-n577, April-2024, Available :http://www.jetir.org/papers/JETIR2404D69.pdf

"CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppn572-n577, April-2024, Available at : http://www.jetir.org/papers/JETIR2404D69.pdf