Classification of plant diseases using machine and deep learning

Lamba, Monika; Gigras, Yogita; Dhull, Anuradha

doi:10.1515/comp-2020-0122

Cited by 30 publications

(11 citation statements)

References 33 publications

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“…Evaluation indicators of each classifier: “Precision” means the ratio of the number of correctly classified samples of a certain category to the predicted samples of this category; “Recall” means the ratio of the number of correctly classified samples of a certain category to the real number of the category, f1 is the comprehensive evaluation of “precision” and “recall”; “accuracy” means the proportion of the number of samples that are correctly predicted; “support” means the number of samples ( Lamba et al., 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning holds immense potential for enhancing accuracy. Hence, the fusion of spectral data and machine learning techniques has been harnessed for leaf disease diagnosis ( Lamba et al., 2021 ). Nevertheless, spectral data often carries inherent noise, making the careful selection of appropriate algorithms and models paramount in the accurate identification of tobacco diseases.…”

Section: Introductionmentioning

confidence: 99%

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Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques

Chen,

Han,

Liu

et al. 2023

Front. Plant Sci.

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Tobacco Mosaic Virus (TMV) and Potato Virus Y (PVY) pose significant threats to crop production. Non-destructive and accurate surveillance is crucial to effective disease control. In this study, we propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infection. Initially, we applied three preprocessing methods – Multivariate Scattering Correction (MSC), Standard Normal Variate (SNV), and Savitzky-Golay smoothing filter (SavGol) – to corrected the leaf full-length spectral sheet data (350-2500nm). Subsequently, we employed two classifiers, support vector machine (SVM) and random forest (RF), to establish supervised classification models, including binary classification models (healthy/diseased leaves or PVY/TMV infected leaves) and six-class classification models (healthy and various severity levels of diseased leaves). Based on the core evaluation index, our models achieved accuracies in the range of 91–100% in the binary classification. In general, SVM demonstrated superior performance compared to RF in distinguishing leaves infected with PVY and TMV. Different combinations of preprocessing methods and classifiers have distinct capabilities in the six-class classification. Notably, SavGol united with SVM gave an excellent performance in the identification of different PVY severity levels with 98.1% average precision, and also achieved a high recognition rate (96.2%) in the different TMV severity level classifications. The results further highlighted that the effective wavelengths captured by SVM, 700nm and 1800nm, would be valuable for estimating disease severity levels. Our study underscores the efficacy of integrating hyperspectral technology and machine learning, showcasing their potential for accurate and non-destructive monitoring of plant viral diseases.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques

Chen,

Han,

Liu

et al. 2023

Front. Plant Sci.

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“… Benchmark against other models [ 27 , 47 , 48 , 49 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]. …”

Section: Figurementioning

confidence: 99%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

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“…The proposed WOANN-based system obtains improved accuracy and outperforms conventional disease prediction techniques, demonstrating promising results. Many other researchers [10]- [13], also used nature inspired optimization along with deep learning for plant disease classification. Applications of varied other ML techniques for plant diseases classification can be found at [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

An ensemble deep learning model for automatic classification of cotton leaves diseases

Kukadiya,

Arora,

Meva

et al. 2024

IJEECS

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<p>Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models.</p>

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Classification of plant diseases using machine and deep learning

Cited by 30 publications

References 33 publications

Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques

Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

An ensemble deep learning model for automatic classification of cotton leaves diseases

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