A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection

Pandian, J. Arun; Kanchanadevi, K.; Kumar, Vinod; Jasińska, Elżbieta; Goňo, Radomír; Leonowicz, Zbigniew; Jasiński, Michał

doi:10.3390/electronics11081266

Cited by 59 publications

(22 citation statements)

References 31 publications

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“…Among deep learning methods, the CNN model has the advantages of high speed and high accuracy. It has been widely applied to identify crop diseases [14,16].…”

Section: Discussionmentioning

confidence: 99%

“…After GoogLeNet was trained with two methods of training from scratch and isomorphic transfer learning, the accuracy rates were 98.36% and 99.35%, respectively [15]. Pandian et al proposed a CNN model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization techniques [16]. The results show that the model achieved an accuracy of 98.41% and illustrate the importance of data augmentation techniques and hyperparameter optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

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GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

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“…Among deep learning methods, the CNN model has the advantages of high speed and high accuracy. It has been widely applied to identify crop diseases [14,16].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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“…The authors of [31] proposed a computerized solution for video testing that was able to achieve a 94% accuracy for positively classifying calcium oxalate crystals. In [32], a computerized system was proposed incorporating an infrared camera and liquid crystal shutter glasses in order to emulate analyticaltesting. In [33], the authors used an automatic electron micrograph analysis technique to detect calcium oxalate crystals, but were not able to determine their progression.…”

Section: Electron Micrograph Processing For the Detection Of Calcium ...mentioning

confidence: 99%

Computerized Detection of Calcium Oxalate Crystal Progression

Mahmoud

2022

Crystals

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Calcium oxalate crystals in plants can cause health issues such as kidney stones if ingested in large amounts. Calcium oxalate crystallizations affect approximately 4% of plants. Some of these crystallizations are more common, and human and animal ingestion can be avoided if the degree of severity is detected at an early stage. Therefore, in this paper, we present a computerized method for detecting calcium oxalate crystallizations at an early stage, when chances for avoiding it are higher. In our research, electron micrograph processing techniques are used to extract features and measure the degree of crystallization progression in cases of crystalized plants and normal plants. A new fast search algorithm—ODS: One Direction Search—is proposed to detect calcium oxalate crystal progression. The calcium oxalate crystal progression is detected on the basis of electron micrographs of calcium oxalate crystals by means of a temporal test. We employed deep learning for feature extraction. The deep learning technique uses transfer learning, which allows the proposed detection model to be trained on only a small amount of data regarding calcium oxalate crystals for the determination of the presence of calcium oxalate crystals and the severity of the cases. The experimental results, using electron micrographs of 6900 clusters, demonstrated a success rate of 97.5% when detecting cases of calcium oxalate crystals. The simulation results of the new temporal algorithm show an enhancement of the speed by 70% compared to well-known temporal algorithms, and increased accuracy when computing PRSN against other algorithms.

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“…Computer vision and machine learning techniques have been employed recently in a variety of crops to accurately diagnose diseases and pest attacks based on characteristic symptoms [5][6][7]. Tis approach relies on the extraction of features from the leaf images and their identifcation and classifcation using an artifcial neural network (ANN) [8].…”

Section: Introductionmentioning

confidence: 99%

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

Pandian

Nisha

Kanchanadevi

et al. 2023

Computational Intelligence and Neuroscience

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We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Conv) layers, and three dense layers. The bottleneck blocks contain depthwise, standard, and linear convolution layers. A single-lens reflex digital image camera was used to collect 1320 images of tea leaves from the North Bengal region of India for preparing the tea grey blight disease dataset. The nongrey blight diseased tea leaf images in the dataset were categorized into two subclasses, such as healthy and other diseased leaves. Image transformation techniques such as principal component analysis (PCA) color, random rotations, random shifts, random flips, resizing, and rescaling were used to generate augmented images of tea leaves. The augmentation techniques enhanced the dataset size from 1320 images to 5280 images. The proposed DCNN model was trained and validated on 5016 images of healthy, grey blight infected, and other diseased tea leaves. The classification performance of the proposed and existing state-of-the-art techniques were tested using 264 tea leaf images. Classification accuracy, precision, recall, F measure, and misclassification rates of the proposed DCNN are 98.99%, 98.51%, 98.48%, 98.49%, and 1.01%, respectively, on test data. The test results show that the proposed DCNN model performed superior to the existing techniques for tea grey blight disease detection.

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A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf Disease Detection

Cited by 59 publications

References 31 publications

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Computerized Detection of Calcium Oxalate Crystal Progression

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

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