Detecting Foliar Diseases in Potato Crops Through a Network of Convolutional Neurons
Abhishek Bajpai, Mohini Tyagi, Bdk Patro, Shashank Yadav
Abstract
Many robust and efficient techniques are being designed to boost production for crops in the agriculture industry. Potatoes are one of the most significant crops for various uses. In this study, we examined potato leaves to detect diseases related to crops. The authors designed an efficient deep learning-based model to identify late blight and early blight illnesses in potato crops. This study proposed a sequential model for detecting diseases in the potato crop. Different classifiers based on deep learning models were then used to analyze the findings. A deep learning architecture built on an image-segmented convolutional neural network with has reached its maximum efficiency of 98.44%. The comparison of the performance of the proposed method and ensemble models showed that the proposed approach outperformed the start-of-the-art deep learning model.