Volume 15 , Issue 1 , PP: 89-100, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Bhavani Vasantha 1 * , G. Pradeepini 2
Doi: https://doi.org/10.54216/JCIM.150108
Crop yield prediction is performed based on crop, water, soil and environmental parameters, which is now a potential research field. Machine-learning approaches are extensively utilized for extracting significant crop features. ML approaches help in handling the issues over the crop prediction process. Some essential issues like linear and non-linear data mapping among the crop yielding values and input data need to be analyzed. However, the performance relies on the quality of extracted features. Here, a novel dense convolutional Network model with a kernel is designed to resolve the challenges identified. Based on feature learning, the anticipated model predicts the crop yielding value and linearly maps the crop yielding output with a nominal threshold value. Here, MATLAB 2020a simulator is used and various metrics like precision, accuracy, recall, F1-score, MAPE, RMSE and value are evaluated with various approaches. The model shows a superior trade-off than other approaches and intends to give better prediction accuracy. The model preserves the original data without disturbing the overall incoming values.
Crop yield prediction , Machine learning , Support vector machine , Classification , Feature learning
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