Harvest Helper: AI-Driven Crop Selection Model
Himanshu S. Bardhiya, Mutyala Pavitra, Ashutosh Bagde, Vansh A Bardhiya · 2025
To maximize agricultural output, this study presents Harvest Helper, an AI-driven crop selection algorithm that makes use of data on market trends, soil health, and environmental variables. Traditional crop selection techniques frequently fall short of keeping up with the complexity of contemporary agriculture as the world's food need grows. The Harvest Helper model uses cutting-edge machine learning algorithms to give farmers customized crop suggestions based on real-time data, increasing yield and resource efficiency. In an extensive analysis including 600 farms in various farming zones, the model showed a 20% average output gain and a 30% decrease in water and fertilizer use. Furthermore, an 85% satisfaction rating with the model's use and performance was reported by farmers in their comments. This study demonstrates how AI has the power to revolutionize farming methods and provide a long-term answer to future food production problems. Recommendations for future study to further enhance and increase the application of the model are presented, along with limitations pertaining data quality and technology accessibility. Accurately predicting crop yields is crucial to resource management and sustainable agriculture. This study examines the effectiveness of three machine learning classifiers on a dataset comprising soil and environmental parameters: Random Forest, Decision Tree, and Logistic Regression. Based on our findings, the Random Forest model performs noticeably better than the other classifiers, suggesting that it might be a reliable tool for crop prediction.