A vegetable disease recognition model for complex background based on region proposal and progressive learning

Zhou, Ji; Li, Jiuxi; Wang, Chun‐Shan; Wu, Haiyuan; Zhao, Chunjiang; Wang, Qian

doi:10.1016/j.compag.2021.106101

Cited by 34 publications

(18 citation statements)

References 17 publications

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“…The CNN architectural models are based on the visual cortex of cats in Hubel's and Wiesel's earlier works. Particularly, [4] performs the object detection task by the use of deep CNN. It has tracked by the appearance of numerous enhanced approaches and application areas of CNN.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Based Rice Plant Disease Detection Model

Narmadha¹,

Sengottaiyan²,

Kavitha³

2022

Intelligent Automation &Amp; Soft Computing

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In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant disease into three classes namely Bacterial Leaf Blight, Brown Spot, and Leaf Smut. Initially, preprocessing takes place in three levels namely channel separation, grayscale conversion, and noise removal using median filtering (MF). Then, the fuzzy c-means (FCM) based segmentation process identifies the diseased portion in the rice plant image. The pretrained DenseNet169 technique is used as a feature extractor and the final layer is replaced by the MLP to perform rice plant disease classification. The effectiveness of the proposed model has been validated against benchmark dataset and the simulation outcome is examined under diverse measures. The obtained results defined the superior results of the DenseNet169-MLP model over the recently presented methods with the maximum accuracy of 97.68%.

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Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Based Rice Plant Disease Detection Model

Narmadha¹,

Sengottaiyan²,

Kavitha³

2022

Intelligent Automation &Amp; Soft Computing

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“…Barbedo et al augmented their plant disease image database by combining the individual lesions and spots on every image, and their convolutional neural network fulfilled a performance improvement for disease identification [ 21 ]. Zhou et al proposed a progressive detection model for vegetable disease through locating the interested region first, which provided an impressive perspective that it was possible to achieve superior results with the help of innovative model structure [ 22 ]. Moreover, Bari et al put the faster region convolutional neural network (Faster-RCNN) into application to diagnose the rice leaf disease [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Zhang

Feng

et al. 2022

Plant Methods

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Background Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. Results In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS–NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. Conclusions This study illustrated that informative VIS–NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.

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“…In order to further improve the identification performance in complex field environments, Ramcharan et al (2017) captured 2,756 cassava leaf images in the field environment and applied Inception v3 to identify three varieties of diseases and two types of insect pests; the average accuracy reached 93%. Zhou et al (2021) adopted progressive learning to guide the model to focus on key feature regions in the disease images; eventually, they achieved a disease identification accuracy of 98.26% in complex backgrounds. Kundu et al (2021) developed a pearl millet disease detection and classification framework based on the Internet of things and deep transfer learning and reported a classification accuracy of 98.78%.…”

Section: Introductionmentioning

confidence: 99%

A Vegetable Leaf Disease Identification Model Based on Image-Text Cross-Modal Feature Fusion

et al. 2022

Self Cite

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In view of the differences in appearance and the complex backgrounds of crop diseases, automatic identification of field diseases is an extremely challenging topic in smart agriculture. To address this challenge, a popular approach is to design a Deep Convolutional Neural Network (DCNN) model that extracts visual disease features in the images and then identifies the diseases based on the extracted features. This approach performs well under simple background conditions, but has low accuracy and poor robustness under complex backgrounds. In this paper, an end-to-end disease identification model composed of a disease-spot region detector and a disease classifier (YOLOv5s + BiCMT) was proposed. Specifically, the YOLOv5s network was used to detect the disease-spot regions so as to provide a regional attention mechanism to facilitate the disease identification task of the classifier. For the classifier, a Bidirectional Cross-Modal Transformer (BiCMT) model combining the image and text modal information was constructed, which utilizes the correlation and complementarity between the features of the two modalities to achieve the fusion and recognition of disease features. Meanwhile, the problem of inconsistent lengths among different modal data sequences was solved. Eventually, the YOLOv5s + BiCMT model achieved the optimal results on a small dataset. Its Accuracy, Precision, Sensitivity, and Specificity reached 99.23, 97.37, 97.54, and 99.54%, respectively. This paper proves that the bidirectional cross-modal feature fusion by combining disease images and texts is an effective method to identify vegetable diseases in field environments.

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A vegetable disease recognition model for complex background based on region proposal and progressive learning

Cited by 34 publications

References 17 publications

Deep Transfer Learning Based Rice Plant Disease Detection Model

Deep Transfer Learning Based Rice Plant Disease Detection Model

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

A Vegetable Leaf Disease Identification Model Based on Image-Text Cross-Modal Feature Fusion

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