A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Abstract

Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

An advanced deep learning models-based plant disease detection: A review of recent research

“…Deep CNN ( Shoaib et al., 2022a ; Shoaib et al., 2022b )is a type of feedforward AI model that is consisting of several hidden layers of convolutional and pooling layers, the CNN model are the best of the DL model for achieving higher detection accuracy using imaging data The CNN model consist of two blocks, the features learning and classification blocks. The features learning block extract various kind of features using the convolutional layer where the features learning is performed at the fully connected layers.…”

“…CANet neural network-based disease detection and ROI segmentation (Shoaib et al, 2022b). Shoaib et al 10.3389/fpls.2023.1158933…”

Cropformer: A new generalized deep learning classification approach for multi-scenario crop classification

“…In contrast to classical machine learning, deep learning no longer requires manually designed features, but can learn complex semantic features from high-dimensional data. Currently, deep learning has been widely used in agriculture due to its effectiveness, including crop classification (Kussul et al, 2017;Minh et al, 2018), pest and disease detection (Akbar et al, 2022;Shoaib et al, 2022a;Shoaib et al, 2022b), yield estimation (Nevavuori et al, 2019;Khaki et al, 2020), etc. The complex network structure of deep learning requires a large amount of labeled data for support, which creates difficulties for the agricultural fields where deep learning is applied.…”