AgriDet: Plant Leaf Disease severity classification using agriculture detection framework. (March 2023) - Pal, Arunangshu; Kumar, Vinay
Abstract: In the field of modern agriculture, plant disease detection plays a vital role in improving crop productivity. To increase the yield on a large scale, it is necessary to predict the onset of the disease and give advice to farmers. Previous methods for detecting plant diseases rely on manual feature extraction, which is more expensive. Therefore, image-based techniques are gaining interest in the research area of plant disease detection. However, existing methods have several problems due to the improper nature of the captured image, including improper background conditions that lead to occlusion, illumination, orientation, and size. Also, cost complexity, misclassifications, and overfitting problems occur in several real-time applications. To solve these issues, we proposed an Agriculture Detection (AgriDet) framework that incorporates conventional Inception-Visual Geometry Group Network (INC-VGGN) and Kohonen-based deep learning networks to detect plant diseases and classify the severity level of diseased plants. In this framework, image pre-processing is done to remove all the constraints in the captured image. Then, the occlusion problem is tackled by the proposed multi-variate grabcut algorithm for effective segmentation. Furthermore, the framework performs accurate disease detection and classification by utilizing an improved base network, namely a pre-trained conventionally based INC-VGGN model. Here, the pre-trained INC-VGGN model is a deep convolutional neural network for prediction of plant diseases that was previously trained for the distinctive dataset. The pre-trained weights and the features learned in this base network are transferred into the newly developed neural network to perform the specific task of plant disease detection for our dataset. In order to overcome the overfitting problem, a dropout layer is introduced, and the deep learning of features is performed using the Kohonen learning layer. After percentage computation, the improved base network classifies the severity classes in the training sets. Finally, the performance of the framework is computed for different performance metrics and achieves better accuracy than previous models. Also, the performance of the statistical analysis is validated to prove the results in terms of accuracy, specificity, and sensitivity.