Citrus disease detection and classification using end-to-end anchor-based deep learning model

Syed‐Ab‐Rahman, Sharifah Farhana; Hesamian, Mohammad Hesam; Prasad, Mukesh

doi:10.1007/s10489-021-02452-w

Cited by 121 publications

(44 citation statements)

References 49 publications

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“…For training tip-burn CNN classification, we used 30 out of 43 images and compared our work with Gozzovelli et al ( 2021 ), where DarkNet was used. For classification of the plant disease datasets, we considered the following recent works: Sujatha et al ( 2021 ), Khattak et al ( 2021 ), and Syed-Ab-Rahman et al ( 2022 ) for CitrusLeaves; Mohameth et al ( 2020 ), Mohanty et al ( 2016 ), Chen et al ( 2020 ), Agarwal et al ( 2020 ), and Abbas et al ( 2021 ) for PlantVillage; for PlantLeaves, we expose only our approach as there are no recent contributions.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Hassan and Maji ( 2022 ) obtain significant results on three datasets: 99.39% on PlantVillage, 99.66% on Rice, and 76.59% on imbalance cassava. Syed-Ab-Rahman et al ( 2022 ) obtained 94.37% accuracy in detection and an average precision of 95.8% on the Citrus leaves dataset, distinguishing between three different citrus diseases, namely citrus black spot, citrus bacterial canker, and Huanglongbing.…”

Section: Related Work On Disease Detectionmentioning

confidence: 99%

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Detection and Localization of Tip-Burn on Large Lettuce Canopies

Franchetti¹,

Pirri

2022

Front. Plant Sci.

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Recent years have seen an increased effort in the detection of plant stresses and diseases using non-invasive sensors and deep learning methods. Nonetheless, no studies have been made on dense plant canopies, due to the difficulty in automatically zooming into each plant, especially in outdoor conditions. Zooming in and zooming out is necessary to focus on the plant stress and to precisely localize the stress within the canopy, for further analysis and intervention. This work concentrates on tip-burn, which is a plant stress affecting lettuce grown in controlled environmental conditions, such as in plant factories. We present a new method for tip-burn stress detection and localization, combining both classification and self-supervised segmentation to detect, localize, and closely segment the stressed regions. Starting with images of a dense canopy collecting about 1,000 plants, the proposed method is able to zoom into the tip-burn region of a single plant, covering less than 1/10th of the plant itself. The method is crucial for solving the manual phenotyping that is required in plant factories. The precise localization of the stress within the plant, of the plant within the tray, and of the tray within the table canopy allows to automatically deliver statistics and causal annotations. We have tested our method on different data sets, which do not provide any ground truth segmentation mask, neither for the leaves nor for the stresses; therefore, the results on the self-supervised segmentation is even more impressive. Results show that the accuracy for both classification and self supervised segmentation is new and efficacious. Finally, the data set used for training test and validation is currently available on demand.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Work On Disease Detectionmentioning

confidence: 99%

Detection and Localization of Tip-Burn on Large Lettuce Canopies

Franchetti¹,

Pirri

2022

Front. Plant Sci.

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“…Sharif et al (2018) proposed an automatic classification method for citrus diseases based on optimized weighted segmentation and feature selection, in which the optimized weighted segmentation algorithm extracted citrus lesions very efficiently, consisting of PCA scores, entropy and precision covariance vectors The hybrid feature selection method of can obtain the best features for post-classification. Syed-Ab- Rahman et al (2021) proposed a two-stage deep CNN model. The two stages are to extract the potential disease target area and use the classifier to classify the potential target area.…”

Section: Related Workmentioning

confidence: 99%

DS-MENet for the classification of citrus disease

Liu

Zhou

et al. 2022

Front. Plant Sci.

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Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

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“…Aim to make the model pay more attention to the key regions of the disease in the image and learn the visual features of the disease from the key areas, the researchers designed a two-stage target detection and recognition network. The main regions in the image containing lesions are first located by the detector, and then these regions are classified by the classifier (Arsenovic et al, 2019;Syed-Ab-Rahman et al, 2022). However, in practice, it is found that there are still limitations in disease identification relying solely on the visual features of leaves.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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Citrus disease detection and classification using end-to-end anchor-based deep learning model

Cited by 121 publications

References 49 publications

Detection and Localization of Tip-Burn on Large Lettuce Canopies

Detection and Localization of Tip-Burn on Large Lettuce Canopies

DS-MENet for the classification of citrus disease

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

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