Modelling a Dense Convolutional Model for Crop Yield Prediction Using Kernel Computation

Bhavani Vasantha^1,*, G Pradeepini¹

¹Department of computer Science and Engineering, Koneru lakshmaiah Education Foundation, Guntur, India

Email: vasanthabhavani@kluniversity.in; pradeepini_cse@kluniversity.in

Abstract

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.

Keywords: Crop yield prediction; Machine learning; Support vector machine; Classification; Feature learning