Crop Selection and Yield Prediction using Machine Learning Approach

Pritesh Patil*, Pranav Athavale, Manas Bothara, Siddhi Tambolkar and Aditya More 

Department of Information Technology, AISSMS Institute of Information Technology, Pune, India.

Corresponding Author Email: pritesh.patil@aissmsioit.org

DOI : http://dx.doi.org/10.12944/CARJ.11.3.26

Abstract:

In recent years, Agriculture sector has been researched a lot with the advancements in technologies like machine learning and smart computing. With the dynamic economics of Agri-produce, it is becoming challenging for farmers to utilize the land efficiently to get maximum profit in the specific landscape. Crop Yield Prediction (CYP) is crucial and is greatly dependent on environmental factors like soil contents, humidity, rainfall as well as area under cultivation and other required metrics. Due to insufficient incorporation of the multiple environmental circumstances, a number of existing tools and techniques used for CYP, such as historical averages, tend to produce inaccurate findings. In such situation, with multiple options of crop, it is essential for farmers to plan the crop strategy in advance. If the farmer can get estimate of the crop yield in advance, cultivation can be done accordingly. To solve this problem, machine learning approach is implemented as a base for accurate predictions. Crop prediction is done by classification model and yield prediction uses regression models to learn from the data. Multiple ML models are analyzed based on performance metrics. Best performer model is incorporated in backend. Among the used models for yield prediction, Random Forest Regression gives best results with MAE of 0.64 and R2 score of 0.96. For crop prediction, Naïve Bayes classifier gives most accurate results with accuracy of 99.39. The study emphasizes how machine learning could revolutionize crop management techniques by giving farmers insights about optimizing resource allocation and boost overall crop yield.

Keywords:

Crop Yield Prediction; Digital Agriculture; Machine Learning; Naïve Bayes; Random Forest

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