MULTINET: A Multi-Agent DRL and EfficientNet Assisted Framework for 3D Plant Leaf Disease Identification and Severity Quantification.

Agriculture plays the vigorous role in economy as well as it is contemplated to be the mainstay of economic system in emerging countries. Furthermore, it influences the society in huge ways including ancillary livelihoods, anyhow occurring of plant diseases impacts many farmers. Henceforth, erroneous leaf disease identification inclines to menacing effects on plants thereby curtailing product quality, productivity or quantity. To overcome these issues, we have designed Multi-agent DRL and EfficientNet assisted 3D plant leaf disease identification and severity estimation (MULTINET) framework. The main aim of this study is to effectively identify plant leaf diseases and quatify the severity of the disease. The proposed work comprehends of following four consecutive processes. At first, we have performed image pre-treatment for data cleansing using Adaptable LoW Pass Weiner (AWW) filter and then EMbellished Manta-Ray Optimization Algorithm (EMMARO) based data augmentation for enhancing image quality and balance the classes. After that, we have adopted Block Divider Model (BDM) for transfiguring 2D to 3D image which sustenance to navigate the information from multiple perceptive and bargains superior viewpoints from miscellaneous angles. Following that, we have executed dual effective segmentation on 3D image by utilizing Enhanced Deep Q-Network (EnDeep) that embrace of Haar-U-Net (HUNT) Algorithm for appropriate feature extraction and Convolution Feature Attention (CFA) Mechanism for dimensionality reduction. Here, the Multiple Deep Reinforcement Learning (DRL) agents are employed for segmentation and ensuring its accuracy through segmentation reliability checker which accommodates rewards entrenched on its actions. Based on those segmented regions, we have accomplished species detection and disease classification by the proposed Lightweight EfficientNet and Di-attention aided Bilinear-VGG (EBBI) Algorithm. At last, the severity enumeration is performed by contemplating lesions counts and its density. The proposed MULTINET framework is conducted in MATLAB R2020a simulation tool and the performance of proposed work is evaluated in terms of accuracy, precision, sensitivity, F1-score and ROC curve where the proposed work outperforms existing works. The accuracy of the proposed work increases from 19%-29%, precision increases from 19%-28%, sensitivity increases from 31%-38%, F1-score increases from 31%-38%, and ROC curve increases about 0.15–0.22 when compared with the existing works.