Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases

Yang, Guofeng; Chen, Guipeng; He, Yong; ZhiYan, Yan; Guo, Yang; Ding, Jian

doi:10.1109/access.2020.3039345

Cited by 46 publications

(21 citation statements)

References 57 publications

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“…Discriminative regions usually help to represent the object; hence, a larger number of discriminative regions can help to improve the classification performance (Wang et al, 2019 ; Yang et al, 2020a ). We use different numbers of attention maps ( M ) for experiments, as shown in Table 7 .…”

Section: Resultsmentioning

confidence: 99%

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

et al. 2021

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The accurate classification of crop pests and diseases is essential for their prevention and control. However, datasets of pest and disease images collected in the field usually exhibit long-tailed distributions with heavy category imbalance, posing great challenges for a deep recognition and classification model. This paper proposes a novel convolutional rebalancing network to classify rice pests and diseases from image datasets collected in the field. To improve the classification performance, the proposed network includes a convolutional rebalancing module, an image augmentation module, and a feature fusion module. In the convolutional rebalancing module, instance-balanced sampling is used to extract features of the images in the rice pest and disease dataset, while reversed sampling is used to improve feature extraction of the categories with fewer images in the dataset. Building on the convolutional rebalancing module, we design an image augmentation module to augment the training data effectively. To further enhance the classification performance, a feature fusion module fuses the image features learned by the convolutional rebalancing module and ensures that the feature extraction of the imbalanced dataset is more comprehensive. Extensive experiments in the large-scale imbalanced dataset of rice pests and diseases (18,391 images), publicly available plant image datasets (Flavia, Swedish Leaf, and UCI Leaf) and pest image datasets (SMALL and IP102) verify the robustness of the proposed network, and the results demonstrate its superior performance over state-of-the-art methods, with an accuracy of 97.58% on rice pest and disease image dataset. We conclude that the proposed network can provide an important tool for the intelligent control of rice pests and diseases in the field.

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Section: Resultsmentioning

confidence: 99%

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

et al. 2021

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“…A subset of this dataset contains nine tomato leaf diseases and one healthy class that has been utilized by most of the recent deep learning-based works on tomato leaf disease classification. Several works on tomato leaf diseases also focused on segmenting leaves from complex backgrounds [32], real-time localization of diseases [33]- [35], detection of leaf disease in early-stage [36], visualizing the learned features of different layers of CNN model [37], [38], combining leaf segmentation and classification [39], and so on. These works mostly targeted removing the restrictions of lighting conditions and uniformity of complex backgrounds.…”

Section: Related Workmentioning

confidence: 99%

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

et al. 2022

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To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset shows that the proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations, making it a suitable choice for low-end devices. Our codes and models will be made available upon publication.

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“…Crowd‐sourced platform is used to gather experienced farmers, cultivators, and experts in agriculture for identifying the dynamic characteristics of wheat diseases. Reference 23 developed a collaborative multinetwork which consists of three networks namely location, feedback, and classification network (LFC‐net) for detecting the diseases on tomato images. The location network detects the information in tomato images and informative regions are used by classification networks to classify the tomato images.…”

Section: Related Workmentioning

confidence: 99%

Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

Kandasamy¹,

Rajendran

2022

Concurrency and Computation

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In recent years, many countries prolonged their research in the field of agricultural farming for yielding more quantity food products with good quality. Agricultural farming is closely related to every country's economy in significant way. To improve the grain productivity in a notable way, plants must be protected from various viral, fungal, and bacterial diseases. The timely identification of plant disease helps to protect the plant at the early stage of the disease else it will spoil the entire plant. In this article, the plant disease identification problem is handled using our novel proposed network where it combines dilated convolution with residual dense block (DCRDB) along with multi-level feature detection (MLFD) for selecting the appropriate feature and bidirectional long short-term memory (Bi-LSTM) classifier for leaf disease prediction. In DCRDB, the dilated convolution derives larger receptive field without loss of resolution to extract greater number of local features from the leaf images and residual dense block expands the size of feature map by densely connecting the layers of convolution. In addition, MLFD network integrates the high-level features with the resulted features of DCRDB. After feature extraction, Bi-LSTM is employed to recognize the leaf disease which supports additional training in both forward and backward direction. Through a set of collected cassava leaf images our research carries forward. The proposed model accuracy is verified with three different datasets such as cassava disease classification dataset, self-made cassava disease dataset and plant village dataset.The performance of the proposed model is compared with some existing methods.Experimental results show that the highest F1-score value of 95.49% attained by our proposed model for identifying cassava leaf disease. K E Y W O R D Sagriculture, cassava leaf disease, dilated convolution, leaf disease identification, residual dense block, multilevel feature detection, plant disease INTRODUCTIONIn the field of agriculture, crop productivity is a major key challenge in many countries. Hence, it is necessary to increase the crop productivity that saves the people's lives in poverty and supports the wealth of the country. One of the major ways to increase the yield is to protect the crops from various viral, fungal, and bacterial diseases. 1 At the initial stage of the disease, the leaves of the plant are first affected, and then it spoils the entire

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Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases

Cited by 46 publications

References 57 publications

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification

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