Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Saleem, Rabia; Shah, Jamal Hussain; Sharif, Muhammad; Yasmin, Mussarat; Yong, Hwan-Seung; Cha, Jaehyuk

doi:10.3390/app112411901

Cited by 38 publications

(13 citation statements)

References 34 publications

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“…Comparing the proposed model to other similar detection models, the proposed model is found more time efficient. A novel deep-learning architecture is developed for the detection and recognition of mango plant diseases ( Saleem et al., 2021 ). The proposed CNN is trained on 754 unhealthy and 780 healthy mango leaf image data.…”

Section: Related Workmentioning

confidence: 99%

“…After picture segmentation, the author examined the accuracy of the technique using different classification algorithms, such as k mean clustering and SVM ( Bargelloni et al., 2021 ). In this paper, the author uses a convolution neural network technique to diagnose various plant illnesses ( Production et al., 2014 ; Saleem et al., 2021 ). The author has conducted an exhaustive study.…”

Section: Related Workmentioning

confidence: 99%

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A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Shoaib

Shah²,

Hussain³

et al. 2022

Front. Plant Sci.

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Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Shoaib

Shah²,

Hussain³

et al. 2022

Front. Plant Sci.

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“…model suffers from a lack of training data. In [16] authors proposed a novel segmentation approach to segment the diseased part by considering the vein pattern of the mango diseased leaf. Diseases treated in this study are sooty mold and powdery mildew.…”

Section: Automatic Diagnosis Based On Machine Learningmentioning

confidence: 99%

“…• The proposed models do not provide real-time diagnosis of mango diseases. This would be very beneficial for mango growers and plant pathologists [16].…”

Section: Potential Challenges In Automatic Diagnosis Of Mango Diseasesmentioning

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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“…Therefore, a computerized method is widely required for diseases detection and classification [11]. A computerized method includes some important steps such as preprocessing of original images, detection of infected parts using image processing techniques, features extraction, reduction of redundant features, and classification using machine learning algorithms [12]. Preprocessing is an important step in agriculture for better visualization of infected part of the crop leaf.…”

Section: Introductionmentioning

confidence: 99%

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Abbas¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets-Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time.

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Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Cited by 38 publications

References 34 publications

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

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

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

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