Identification of peach leaf disease infected by Xanthomonas campestris with deep learning

Zhang, Ke-Ke; Xu, Zheyuan; Dong, Shoukun; Cen, Canjian; Wu, Qiufeng

doi:10.1016/j.eaef.2019.05.001

Cited by 26 publications

(14 citation statements)

References 22 publications

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“…A region-based single-shot multi-box detector was used with Visual Geometry Group (VGG) to detect the cotton plant disease that was proposed [22]. Some of the studies used transfer learning for plant disease detection on various plant datasets [23] [24], peach dataset [25], and pre-trained AlexNet was employed for cucumber [26] [27] and tomato [28]. Pre-trained ResNet was employed for coffee and soybean plant disease detection [29] [30], and DenseNet was employed for apple leaves [31].…”

Section: Literature Surveymentioning

confidence: 99%

Cardamom Plant Disease Detection Approach Using EfficientNetV2

2022

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Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamilnadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U 2 -Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%.

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Section: Literature Surveymentioning

confidence: 99%

Cardamom Plant Disease Detection Approach Using EfficientNetV2

2022

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“…To capture more complex features of the input image and increase the nonlinearity of the deep learning structure, convolutional layers are often followed by activation layers. To reduce the number of parameters and computational complexity of the model, a pooling layer is placed between successive convolutional layers (Zhang et al, 2019b). As a result, this layer helps to prevent overfitting and improve generalization (Zhu et al, 2019).…”

Section: The Convolutional Neural Network Features Extractionmentioning

confidence: 99%

Computer-vision classification of corn seed varieties using deep convolutional neural network

Javanmardi

Ashtiani

Verbeek

et al. 2021

Journal of Stored Products Research

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Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.

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“…In the last few decades, several techniques have been developed to detect leaf diseases in various crops [8,14,15]. In most of the techniques, features were extracted using the image processing techniques, then extracted features were fed to a classification technique.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

et al. 2021

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Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

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Identification of peach leaf disease infected by Xanthomonas campestris with deep learning

Cited by 26 publications

References 22 publications

Cardamom Plant Disease Detection Approach Using EfficientNetV2

Cardamom Plant Disease Detection Approach Using EfficientNetV2

Computer-vision classification of corn seed varieties using deep convolutional neural network

Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

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