Mango leaf disease identification and classification using a CNN architecture optimized by crossover-based levy flight distribution algorithm

Prabu, M.; Chelliah, Balika J.

doi:10.1007/s00521-021-06726-9

Cited by 52 publications

(16 citation statements)

References 33 publications

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“…Negative measures were taken as Type II measures such as false discovery rate (FDR), false negative rate (FNR) and false positive rate (FPR).” This validation analysis was undergone with the population count as 10, and maximum iterative count as 25 for the suggested leaf disease recognition method. The proposed FS‐SSO was compared with other meta‐heuristic algorithms like “particle swarm optimization (PSO) (Xiong et al, 2020), grey wolf optimizer (GWO) (Sathiyabhama et al, 2021), tunicate swarm algorithm (TSA) (Abdolinejhad et al, 2021), SSO (Abedinia et al, 2014) and machine learning algorithms like CNN (Lee et al, 2021; Muppala & Guruviah, 2020), ResNet (Prabu & Chelliah 2022), YOLO (Aly et al, 2021), and Res‐Yolo (Vallabhajosyula et al, 2021). ”…”

Section: Resultsmentioning

confidence: 99%

“…The proposed paddy leaf disease detection method utilized for the ResNet classifier for classifying the segmented leaf images

{\mathrm{IM}}_{i}^{\mathrm{SEG}}

by obtaining its features. In Prabu and Chelliah (2022), the ResNet establishes the enhanced performance while having more number of images. This method is implemented with the help of “skipping connections” that are made on the two to three layers with “ReLU and batch normalization.” The residual learning is applicable to the multiple layers of architecture.…”

Section: Development Of Hybrid Deep Learning With Parameter Optimizationmentioning

confidence: 99%

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Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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Paddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases.Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by "adaptive K-means clustering." This is also accomplished by the Fitness Sorted-Shark Smell Optimization (FS-SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS-SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

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Section: Resultsmentioning

confidence: 99%

“…The proposed paddy leaf disease detection method utilized for the ResNet classifier for classifying the segmented leaf images

{\mathrm{IM}}_{i}^{\mathrm{SEG}}

Section: Development Of Hybrid Deep Learning With Parameter Optimizationmentioning

confidence: 99%

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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“…Another limitation of the proposed model is that the model ignores tiny or invisible defects on the fruit, which reduces the accuracy. In [17] authors proposed a novel framework for mango leaves disease classification namely Anthracnose, Bacterial black spot, and Sooty mold. They used a CNN with crossover-based levy flight distribution for better feature selection, MobileNetV2 model for the learning stage and SVM for diseases classification.…”

Section: Automatic Diagnosis Based On Deep Learningmentioning

confidence: 99%

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

Faye¹,

Diop²,

Dione³

2022

JCC

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The mango crop suffers from several diseases that reduce both the production and the quality of the mangoes. This also reduces its price on the international market. Diagnosis of these diseases remains difficult in many countries due to poverty and lack of infrastructure. Plant pathologists use several techniques to identify these diseases. But these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. In the last decade, researchers have proposed several solutions in the field of automatic diagnosis of mango diseases. Such solutions are based on Machine Learning (ML) and Deep Learning (DL) algorithms. In this paper, we divided these solutions into two groups: solutions based on classical ML algorithms, and those based on DL. In recent years, DL, especially Convolutional Neural Network (CNN) has become the most widely used method by researchers because of its impressive performance. The critical analysis of the proposed solutions has allowed us to identify their limits and potential challenges in mango disease automatic diagnosis.

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“…Prabu and Chelliah [ 35 ] have developed a new method of detecting mango leaf diseases. Over 388 images of healthy and ill subjects (mango anthracnose, soot mold, and bacterial black spot) were selected.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring

Anari

2022

Computational Intelligence and Neuroscience

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As possible diseases develop on plant leaves, classification is constantly hampered by obstacles such as overfitting and low accuracy. To distinguish healthy products from defective ones, the agricultural industry requires precise and error-free analysis. Deep convolutional neural networks are an efficient model of autonomous feature extraction that has been shown to be fairly effective for detection and classification tasks. However, deep convolutional neural networks often require a large amount of training data, cannot be translated, and need a number of parameters to be specified and tweaked. This paper proposes a highly effective structure that can be applied to classifying multiple leaf diseases of plants and fruits during the feature extraction step. It uses a deep transfer learning model that has been modified to serve this purpose. In summary, we use model engineering (ME) to extract features. Multiple support vector machine (SVM) models are employed to enhance feature discrimination and processing speed. The kernel parameters of the radial basis function (RBF) are determined based on the selected model in the training step. PlantVillage and UCI datasets were used to analyze six leaf image sets containing healthy and diseased leaves of apple, corn, cotton, grape, pepper, and rice. The classification process resulted in approximately 90,000 images. During the experimental implementation phase, the results show the potential of a powerful model in classification operations, which will be beneficial for a variety of future leaf disease diagnostic applications for the agricultural industry.

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Mango leaf disease identification and classification using a CNN architecture optimized by crossover-based levy flight distribution algorithm

Cited by 52 publications

References 33 publications

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring

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