A robust deep learning approach for tomato plant leaf disease localization and classification

Abstract

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.

Aggregating Different Scales of Attention on Feature Variants for Tomato Leaf Disease Diagnosis from Image Data: A Transformer Driven Study

“…For feature extraction, the authors have used ResNet in place of VGG16, and the proposed model gave around better accuracy compared to the regular faster RCNN model. A robust approach has been presented for tomato plant leaf disease localization and classification in [ 18 ]. In this paper, the authors have proposed a method based on the faster RCNN.…”

An Analysis of Plant Diseases Identification Based on Deep Learning Methods

“…Faster R-CNN and YOLOv3 algorithms are generally improved according to the actual requirements. The improved Faster R-CNN tends to improve detection accuracy ( Alruwaili et al, 2022 ; Nawaz et al, 2022 ), and the improved YOLOv3 algorithm emphasizes the improvement of detection speed ( Liu and Wang, 2020 ; Tian et al, 2019 ). Hence, balancing and optimizing detection accuracy and detection speed plays a major role in plant disease identification.…”

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

“…Several studies have explored the application of object detection algorithms for the accurate and efficient detection of plant leaf diseases. Nawaz et al [20] proposed an approach named Faster RCNN based on ResNet-34 and Convolutional Block Attention to localize and classify tomato plant leaf disease. The proposed method achieved an accuracy of 99.97% and a mAP of 0.981 using the public dataset PlantVillage.…”