A low shot learning method for tea leaf’s disease identification

Hu, Gensheng; Wu, Haoyu; Zhang, Yan; Wan, Mingzhu

doi:10.1016/j.compag.2019.104852

Cited by 160 publications

(81 citation statements)

References 11 publications

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“…In order to show the generalization ability of our models, we compared the recognition accuracy of our models against existing benchmark deep learning models on another dataset, namely the tea leaves data set [32]. It contains 40 images for each disease, such as leaf blight, red scab, and red leaf spot as shown in Table 3.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…On the other hand, when using the tea leaf dataset [32], the siamese network is trained for 200 epochs after changing the batch size to 8 and the number of batches to 50. For all the other cases, the batch size is changed to 4 and all the other parameters are kept unchanged.…”

Section: A Implementation and Trainingmentioning

confidence: 99%

“…5-fold cross-validation accuracy, precision, recall, and F1 Score comparison using citrus fruits and leaves (Citrus)[20] dataset, and 5-fold cross-validation accuracy comparison using tea leaves (Tea)[32] dataset…”

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confidence: 99%

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Deep Metric Learning Based Citrus Disease Classification With Sparse Data

et al. 2020

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Early recognition of citrus diseases is important for preventing crop losses and employing timely disease control measures in farms. Employing machine learning-based approaches, such as deep learning for accurate detection of multiple citrus diseases is challenging due to the limited availability of labeled diseased samples. Further, a lightweight architecture with low computational complexity is required to perform citrus disease classification on resource-constrained devices, such as mobile phones. This enables the practical utility of the architecture to perform effective monitoring of diseases by farmers using their own mobile devices in the farms. Hence, we propose a lightweight, fast, and accurate deep metric learningbased architecture for citrus disease detection from sparse data. In particular, we propose a patch-based classification network that comprises an embedding module, a cluster prototype module, and a simple neural network classifier, to detect the citrus diseases accurately. Evaluation of our proposed approach using publicly available citrus fruits and leaves dataset reveals its efficiency in accurately detecting the various diseases from leaf images. Further, the generalization capability of our approach is demonstrated using another dataset, namely the tea leaves dataset. Comparison analysis of our approach with existing stateof-the-art algorithms demonstrate its superiority in terms of detection accuracy (95.04%), the number of parameters required for tuning (less than 2.3 M), and the time efficiency in detecting the citrus diseases (less than 10 ms) using the trained model. Moreover, the ability to learn with fewer resources and without compromising accuracy empowers the practical utility of the proposed scheme on resource-constrained devices, such as mobile phones.

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Section: B Results and Discussionmentioning

confidence: 99%

Section: A Implementation and Trainingmentioning

confidence: 99%

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Deep Metric Learning Based Citrus Disease Classification With Sparse Data

et al. 2020

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“…Kerkech et al [24] proposed deep leaning approaches for vine diseases detection using vegetation indices and colorimetric spaces, applied to images collected by UAV. Hu et al [25] proposed a low shot learning method for disease identification in tea leaves. Coulibaly et al [26] proposed an approach for the identification of mildew disease in pearl millet, which is using transfer learning with feature extraction.…”

Section: A Disease Detectionmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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“…Object detection methods have been used to identify diseased regions of grape plants (Kerkech et al, 2018) and diseased leaves of soybean (Tetila et al, 2017). Semantic segmentation of unmanned aerial vehicle (UAV) images, the task we undertake here, has been implemented in soybean (Tetila et al, 2019), tea plants (Gensheng et al, 2019), and maize .…”

Section: Introductionmentioning

confidence: 99%

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data

Wiesner-Hanks

Stewart

et al. 2019

Front. Plant Sci.

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Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.

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A low shot learning method for tea leaf’s disease identification

Cited by 160 publications

References 11 publications

Deep Metric Learning Based Citrus Disease Classification With Sparse Data

Deep Metric Learning Based Citrus Disease Classification With Sparse Data

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data

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