The Plant Pathology 2020 challenge dataset to classify foliar disease of apples

Thapa, Ranjita; Snavely, Noah; Belongie, Serge; Khan, Awais

doi:10.48550/arxiv.2004.11958

Cited by 8 publications

(18 citation statements)

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“…Our framework was built upon a system information of agriculture presented [6,19,9]. Fasoula et al [6] propose selection criteria based on the genetic and epigenetic responses of healthy and superior crops.…”

Section: Related Workmentioning

confidence: 99%

“…Fasoula et al [6] propose selection criteria based on the genetic and epigenetic responses of healthy and superior crops. The work in research [19,9] contributes toward development and deployment of machine learning-based automated plant disease detection by providing repositories of plant images. Thapa et al [19] manually collected 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise.…”

Section: Related Workmentioning

confidence: 99%

“…The work in research [19,9] contributes toward development and deployment of machine learning-based automated plant disease detection by providing repositories of plant images. Thapa et al [19] manually collected 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. They also made a synthetic dataset created for apple scab, cedar apple rust, and healthy leaves available to the public.…”

Section: Related Workmentioning

confidence: 99%

“…Hughes et al [9] shared over 50,000 expertly curated images on healthy and infected leaves of crop plants through an existing online platform PlantVillage. Our experiments were conducted on the Plant Pathology 2020 datasets [19].…”

Section: Related Workmentioning

confidence: 99%

“…Recent advances in machine learning have provided new methods and technologies for classification modeling and object detection. Wang et al [20] present a series of deep convolutional neural networks trained to diagnose the severity of the apple black rot disease using datasets in the PlantVillage [19], which are annotated by botanists with four severity stages. Their best classifier model yields an overall accuracy of 90.4%.…”

Section: Related Workmentioning

confidence: 99%

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A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology

Prasad,

Mehta,

Horak

et al. 2021

Preprint

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Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that is representative of UAV images. The analysis of applications and methods is conducted on a database of high-fidelity apple tree images which are corrupted with class imbalance. The application begins by generating high-fidelity data using generative networks and then uses this novel data alongside the original high-fidelity data to produce low-fidelity images. A machine-learning identifier identifies plants and labels them as potentially diseased or not. A machine learning classifier is then given the potentially diseased plant images and returns actual diagnoses for these plants. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system. Our drone technology shows promising results in accuracy when compared to labor-based methods of diagnosis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology

Prasad,

Mehta,

Horak

et al. 2021

Preprint

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The Enhancement of Classification of Imbalanced Dataset for Edge Computing

Huang

Hsu

Hung

et al. 2022

Communications in Computer and Information Science

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This paper proposes a novel unsupervised domain adaption (UDA) method based on contrastive bi-projector (CBP), which can improve the existing UDA methods. It is called CBPUDA here, which effectively promotes the feature extractors (FEs) to reduce the generation of ambiguous features for classification and domain adaption. The CBP differs from traditional bi-classifier-based methods at that these two classifiers are replaced with two projectors of performing a mapping from the input feature to two distinct features. These two projectors and the FEs in the CBPUDA can be trained adversarially to obtain more refined decision boundaries so that it can possess powerful classification performance. Two properties of the proposed loss function are analyzed here. The first property is to derive an upper bound of joint prediction entropy, which is used to form the proposed loss function, contrastive discrepancy (CD) loss. The CD loss takes the advantages of the contrastive learning and the bi-classifier. The second property is to analyze the gradient of the CD loss and then overcome the drawback of the CD loss. The result of the second property is utilized in the development of the gradient scaling (GS) scheme in this paper. The GS scheme can be exploited to tackle the unstable problem of the CD loss because training the CBPUDA requires using contrastive learning and adversarial learning at the same time. Therefore, using the CD loss with the GS scheme overcomes the problem mentioned above to make features more compact for intra-class and distinguishable for inter-class. Experimental results express that the CBPUDA is superior to conventional UDA methods under consideration in this paper for UDA and fine-grained UDA tasks.

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