Plant Disease Detection using Convolutional Neural Network - Alina Arshad; Syed Hasan Adil; Mansoor Ebrahim (JISR on Computing, 2024)
The current agriculture system of the world is facing a massive challenge to agricultural productivity. Agricultural productivity must be increased to maintain enough food. Due to the various plant diseases, the expected agricultural productivity is impossible to achieve. Using Deep Learning (DL) in the agriculture field, farmers can monitor continuously rather than manually checking plants’ health. ResNet, DenseNet, GoogleNet, Inception, and YOLO are deep learning-based computer vision models. The most significant advantage of Deep Learning is that we do not need to extract features from the image manually. You feed the raw pixels in the images directly as inputs to the network. The network learns to extract features while training the model. The basic objective of this work is to propose multiple convolutional neural network-based models and identify the best model suitable for classifying the individual plant image into healthy and disease (multiple diseases) classes. This will help farmers and agriculturists to have a 360° insight into the cultivation status of the plants. Furthermore, to assess the proposed models; different quality metrics have been computed (i.e., accuracy and loss on the training, validation, and test datasets) to perform the comparative analysis between our proposed and existing techniques. These proposed models achieved a validation accuracy of 97% using MobileNet V1, 96% using MobileNet V1, and 83% using Vanilla CNN. While on 33 challenging images, a test accuracy of 82% using MobileNet V1, 76% using MobileNet V1, and 53% using Vanilla CNN has been achieved.