Plant diseases and pests detection based on deep learning: a review

Li, Jun; Wang, Xuewei

doi:10.1186/s13007-021-00722-9

Cited by 512 publications

(282 citation statements)

References 87 publications

(47 reference statements)

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“…e output of navel oranges in China ranks among the top in the world, but, due to the impact of navel orange diseases and pests, the output and quality of navel oranges have declined to varying degrees. Diseases and pests of navel oranges can be detected visually, because they often affect the shape or color of fruits, leaves, stems, and other parts of the plant [1]. Farmers must detect the diseased parts and conditions as early as possible before the navel orange disease spreads in the plantation.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

Yin-e

Liu

2021

Computational Intelligence and Neuroscience

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The prevention and control of navel orange pests and diseases is an important measure to ensure the yield of navel oranges. Aiming at the problems of slow speed, strong subjectivity, high requirements for professional knowledge required, and high identification costs in the identification methods of navel orange pests and diseases, this paper proposes a method based on DenseNet and attention. The power mechanism fusion (DCPSNET) identification method of navel orange diseases and pests improves the traditional deep dense network DenseNet model to realize accurate and efficient identification of navel orange diseases and pests. Due to the difficulty in collecting data of navel orange pests and diseases, this article uses image enhancement technology to expand. The experimental results show that, in the case of small samples, compared with the traditional model, the DCPSNET model can accurately identify different types of navel orange diseases and pests images and the accuracy of identifying six types of navel orange diseases and pests on the test set is as high as 96.90%. The method proposed in this paper has high recognition accuracy, realizes the intelligent recognition of navel orange diseases and pests, and also provides a way for high-precision recognition of small sample data sets.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

Yin-e

Liu

2021

Computational Intelligence and Neuroscience

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“…In recent years, the use of deep learning for image classification in the area of plant pathogen detection has received much attention. For reviews on this topic see [6,7,28,29]. For visually easily detectable diseases located on the leaf surface like powdery mildew several deep learning image segmentation approaches have been published in recent years [24,30,31].…”

Section: Discussionmentioning

confidence: 99%

High-Throughput Phenotyping of Leaf Discs Infected with Grapevine Downy Mildew Using Shallow Convolutional Neural Networks

et al. 2021

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Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola ((Berk. and Curt.) Berl. and de Toni) causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to considerably reduce the amount of time during leaf disc phenotyping. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.

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“…Liu et al presented the review of deep learning models employed for plant pest and disease prediction [ 54 ]. They highlighted the challenges in applying the deep learning models in plant disease prediction and highlighted the possible solutions for the identified challenges.…”

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

228

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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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Plant diseases and pests detection based on deep learning: a review

Cited by 512 publications

References 87 publications

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

High-Throughput Phenotyping of Leaf Discs Infected with Grapevine Downy Mildew Using Shallow Convolutional Neural Networks

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

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