A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves

Shin, Jaemyung; Chang, Young K.; Heung, Brandon; Nguyen-Quang, Tri; Price, G.W.; Al-Mallahi, Ahmad

doi:10.1016/j.compag.2021.106042

Cited by 89 publications

(33 citation statements)

References 29 publications

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“…On the other hand, Tahir et al [ 122 ] investigated disease identification from the Apple plant using InceptionV3 and shown an overall accuracy of 97% on the PlantVillage data set. Shin et al [ 123 ] depicted the comparative study of six different CNN models to identify powdery mildew disease on strawberry leaves. The optimal models for different parameters, videlicet, accuracy, speed, and hardware requirement have been suggested upon comparison.…”

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

212

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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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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

212

View full text Add to dashboard Cite

show abstract

“…F-score is a weighted average of precision and recall. If the difference between the values of FP and FN is large, then the F-score should be considered firstly [ 40 ]. Among the five CNN models, ResNet50 obtains the highest F-score of 79.79%.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In the traditional machine learning models, the SIFT [ 41 ] and SURF [ 42 ] are selected as the hand-crafted features. SIFT and SURF are the two most widely used features and have been applied in many fields of agriculture, such as crop disease [ 40 , 43 ], crop/weed classification [ 44 ], etc. In addition, the SVM and artificial neural network (ANN) are selected as the classifier.…”

Section: Experiments and Analysismentioning

confidence: 99%

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Qiao

Yao

et al. 2022

Plant Methods

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Background Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. Methods To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and conclusion The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33$$\%$$ % ) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

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“…The F-score is a weighted average of precision and recall. If the difference between the values of FP and FN is large, then the F-score should be considered firstly [40]. Among the five CNN models, ResNet50 obtains the highest F-score of 79.79%.…”

Section: Comparison Of Different Cnnsmentioning

confidence: 99%

“…In traditional machine learning models, the SIFT [41] and SURF [42] are selected as the hand-crafted features. SIFT and SURF are the two most widely used features and have been applied in many fields of agriculture, such as crop disease [40,43], crop/weed classification [44], etc. In addition, the support vector machine (SVM) and artificial neural network (ANN) are selected as the classifier.…”

Section: Comparison Of Traditional Machine Learning and Cnnsmentioning

confidence: 99%

Automatic Freezing-Tolerant Rapeseed Material Recognition Using UAV Images and Deep Learning

Qiao

Yao

et al. 2021

Preprint

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Background: Freezing injury is a serious and common damage that occurs to winter rapeseed during the overwintering period. The freezing injury directly reduces the rapeseed yield and causes serious economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipment. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of experts. Methods: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning technology and unmanned aerial vehicle (UAV) images captured by a consumer drone. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning technology. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials collected by the Phantom 4 Pro. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and Conclusion: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33%) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based approach significantly outperforms the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

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A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves

Cited by 89 publications

References 29 publications

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

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

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Automatic Freezing-Tolerant Rapeseed Material Recognition Using UAV Images and Deep Learning

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