Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress

Esgario, José G. M.; Krohling, Renato A.; Ventura, José Aires

doi:10.48550/arxiv.1907.11561

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The results show that the classification accuracy of convolutional neural network (CNN; 95.83%) was significantly higher than SVM (87.65%). Due to the strong generalization of deep learning, many researchers have realized the classification of plant leaf diseases and pests based on CNN, including, but not limited to tea, wheat, rice, ginkgo, walnut, coffee, cucumber, tomato, apple, and banana leaves, achieved high classification accuracy ( Hu et al, 2019b ; Anagnostis et al, 2020 ; Esgario et al, 2020 ; Karthik et al, 2020 ; Li et al, 2020 ; Zhong and Zhao, 2020 ). In addition to the application of deep learning in the classification of plant leaf diseases and pests, various works have been presented for locating and assessing the damaged areas.…”

Section: Introductionmentioning

confidence: 99%

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level

et al. 2022

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As compared with the traditional visual discrimination methods, deep learning and image processing methods have the ability to detect plants efficiently and non-invasively. This is of great significance in the diagnosis and breeding of plant disease resistance phenotypes. Currently, the studies on plant diseases and pest stresses mainly focus on a leaf scale. There are only a few works regarding the stress detection at a complex canopy scale. In this work, three tea plant stresses with similar symptoms that cause a severe threat to the yield and quality of tea gardens, including the tea green leafhopper [Empoasca (Matsumurasca) onukii Matsuda], anthracnose (Gloeosporium theae-sinensis Miyake), and sunburn (disease-like stress), are evaluated. In this work, a stress detection and segmentation method by fusing deep learning and image processing techniques at a canopy scale is proposed. First, a specified Faster RCNN algorithm is proposed for stress detection of tea plants at a canopy scale. After obtaining the stress detection boxes, a new feature, i.e., RGReLU, is proposed for the segmentation of tea plant stress scabs. Finally, the detection model at the canopy scale is transferred to a field scale by using unmanned aerial vehicle (UAV) images. The results show that the proposed method effectively achieves canopy-scale stress adaptive segmentation and outputs the scab type and corresponding damage ratio. The mean average precision (mAP) of the object detection reaches 76.07%, and the overall accuracy of the scab segmentation reaches 88.85%. In addition, the results also show that the proposed method has a strong generalization ability, and the model can be migrated and deployed to UAV scenarios. By fusing deep learning and image processing technology, the fine and quantitative results of canopy-scale stress monitoring can provide support for a wide range of scouting of tea garden.

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Section: Introductionmentioning

confidence: 99%

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level

et al. 2022

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“…Therefore, deep learning is widely used because it is more accurate than traditional machine learning method in identifying plant diseases and insect pests and can automatically extract features for learning. A series of studies have shown that the application of convolutional neural network (CNN) can realize the high-precision classification of leaf diseases and pests of tea plants, walnut, coffee, tomato, ginkgo, apple, rice and other plants (Anagnostis et al, 2020;Bari et al, 2021;Esgario et al, 2019;Hu et al, 2019;Li et al, 2020;R et al, 2020;Zhao et al, 2022;Zhong & Zhao, 2020). By combining deep learning with image processing, the recognition and differentiation of three types of stress in tea canopy images using Faster RCNN and YOLOv3 were compared (Zhao et al, 2022).…”

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Deep learning and targeted metabolomics‐based monitoring of chewing insects in tea plants and screening defense compounds

Chen,

Wang,

Gao

et al. 2023

Plant Cell & Environment

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Tea is an important cash crop that is often consumed by chewing pests, resulting in reduced yields and economic losses. It is important to establish a method to quickly identify the degree of damage to tea plants caused by leaf‐eating insects and screen green control compounds. This study was performed through the combination of deep learning and targeted metabolomics, in vitro feeding experiment, enzymic analysis and transient genetic transformation. A small target damage detection model based on YOLOv5 with Transformer Prediction Head (TPH‐YOLOv5) algorithm for the tea canopy level was established. Orthogonal partial least squares (OPLS) was used to analyze the correlation between the degree of damage and the phenolic metabolites. A potential defensive compound, (‐)‐epicatechin‐3‐O‐caffeoate (EC‐CA), was screened. In vitro feeding experiments showed that compared with EC and epicatechin gallate, Ectropis grisescens exhibited more significant antifeeding against EC‐CA. In vitro enzymatic experiments showed that the hydroxycinnamoyl transferase (CsHCTs) recombinant protein has substrate promiscuity and can catalyze the synthesis of EC‐CA. Transient overexpression of CsHCTs in tea leaves effectively reduced the degree of damage to tea leaves. This study provides important reference values and application prospects for the effective monitoring of pests in tea gardens and screening of green chemical control substances.

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Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey

Chandra¹,

Desai

Guo³

et al. 2020

Advanced Computing and Communications

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In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.

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Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress

Cited by 3 publications

References 21 publications

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level

Deep learning and targeted metabolomics‐based monitoring of chewing insects in tea plants and screening defense compounds

Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey

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