Sunil C.K.Jaidhar C.D.Patil N.2021-05-052021-05-0520202020 IEEE 15th International Conference on Industrial and Information Systems, ICIIS 2020 - Proceedings , Vol. , , p. 460 - 465https://doi.org/10.1109/ICIIS51140.2020.9342729https://idr.nitk.ac.in/handle/123456789/14771Recognizing the plant disease automatically in real-time by examining a plant leaf image is highly essential for farmers. This work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased. To achieve this, a set of experiments were conducted with three distinct plant leaf datasets. Each of the experiments were conducted by setting kernel size of 3× 3 and each experiment was conducted independently with different epochs i.e., 50, 75, 100, 125, and 150. The MCLCNN classifier achieved minimum accuracy of 87.47% with 50 epochs and maximum accuracy of 99.25% with 150 epochs for the Peach plant leaves. © 2020 IEEE.Conference Paper