aspg-2-3119-1791419464 20261008003104 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2025 15 1 Bhavani Vasantha Department of computer Science and Engineering, Koneru lakshmaiah Education Foundation, Guntur, India G. Pradeepini Department of computer Science and Engineering, Koneru lakshmaiah Education Foundation, Guntur, India 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 style='font-size:10.0pt;mso-ansi-font-size:10.0pt;mso-bidi-font-size:10.0pt; font-family:"Cambria Math",serif;mso-ascii-font-family:"Cambria Math"; mso-hansi-font-family:"Cambria Math";mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi;color:black;mso-themecolor:text1;background: white;font-style:italic;mso-bidi-font-style:normal'> style='mso-bidi-font-style:normal'> font-family:"Cambria Math",serif;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi;color:black;mso-themecolor:text1;background: white'>R normal'> mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-bidi; color:black;mso-themecolor:text1;background:white'>2 style='mso-bidi-font-style:normal'> font-family:"Cambria Math",serif;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-bidi;color:black;mso-themecolor:text1;background: white'> 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. 2025 89 100 3119 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.150108 https://www.americaspg.com/journal/2/article/3119 https://www.americaspg.com/storage/31723396921.pdf