Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Tan, Lijuan; Lu, Jinzhu; Jiang, Huanyu

doi:10.3390/agriengineering3030035

Cited by 89 publications

(44 citation statements)

References 47 publications

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“…Xiang et al [32] established a lightweight convolutional neural network-based network model with channel shuffle operation and multiple-size modules that achieved accuracy levels of 90.6% and 97.9% on a plant disease severity and PlantVillage datasets, respectively. Tan et al [33] compared the recognition effects of deep learning networks and machine learning algorithms on tomato leaf diseases and found that the metrics of the tested deep learning networks are all better than those of the measured machine learning algorithms, with the ResNet34 network obtaining the best results. Alita et al [34] used the EfficientNet deep learning model to detect plant leaf disease, and it was superior to other state-of-the-art deep learning models in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

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Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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“…Many research works have been accomplished related to the TLDIs processing and analysis [1][2][3][4][5][6][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Some of the works are mentioned as follows.…”

Section: Related Workmentioning

confidence: 99%

“…The vegetables can be classified as tomato, potato, cabbage, cauliflower, broccoli, pumpkin, bean, etc. However, vegetable plant leaf diseases [1][2][3][4][5][6] and other diseases greatly hamper the production of vegetables. Tomato leaf diseases are focused in this work.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Intelligent Based Approach for the Classification and Analysis of Tomato Leaf Disease Images

Jena

2022

IJHSP

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Purpose: Vegetable production plays a vital role for the existence of human society. It is very much essential for the proper care of vegetable plants for better production of vegetables. However, vegetable plant leaf disease is a major concern in the current scenario. Tomato leaf disease is one of them. So, preventive measures should be taken to avoid the rise of tomatoes and other leaf diseases at the earliest for better production of vegetables. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of tomato leaf disease images (TLDIs) into the bacterial spot (BS), early blight (EB), late blight (LB), leaf mold (LM), septoria leaf spot (SLS), tomato mosaic virus (TMV), tomato yellow leaf curl virus (TYLCV) and healthy (HL) types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1600 TLDIs having 200 numbers of each type such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of TLDIs into several types such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

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“…Bir başka disiplinler arası gerçekleştirilen çalışmada, yine PlantVillage domates veri kümesi ve domates hastalığı sınıflandırma problemi için en uygun ML/DL modellerinin belirlenmesi amaçlanmıştır [14]. Çalışmada, yerel ikili desen (LBP) ve GLCM yöntemleri kullanılarak 105 renk özelliği çıkarılmıştır.…”

Section: Giriş (Introduction)unclassified

Domates yapraklarında hastalık tespiti için önerilen hafif evrişimli sinir ağı ile önceden eğitilmiş ağların performans karşılaştırması

Ecemis

İlhan

2022

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Domateslerde oluşan hastalıkların çoğu yaprakların gözlemlenmesi ile belirlenebilir. Bu nedenle domates yapraklarının gözlemlenmesi, sebze yetiştiriciliğinde kritik bir öneme sahiptir. Yaprakların üzerinde bulunan izler, renk değişimleri, kıvrıklık vb. hastalığı ya da hastalık riski oluşturabilecek bir virüsü işaret edebilir. Yaprakların sağlığının korunması domateslerin de verimliliğini artıracağından, yaprak gelişimlerinin gözlemlenmesi, olası hastalık risklerinin önceden doğru ve hızlı tespitinin yapılması bir gerekliliktir. Önerilen çalışmada, domates bitkisinin yapraklarında oluşan 9 farklı hastalığı ve sağlıklı yaprakları tespit edebilen "T-LeafNet" Evrişimsel Sinir Ağı (ESA-CNN) modeli önerilmiştir. Önerilen ağ sıfırdan eğitilerek sınıflandırma başarısı ölçülmüştür. Ayrıca, öğrenme aktarımı (transfer learning) kapsamında hazır ağ modellerinden MobileNetV2, AlexNet ve VGG16 mimarileri de veri kümesi üzerinde test edilmiş ve sonuçlar karşılaştırılmıştır. Testler orjinal ve artırılmış veriler üzerinde farklı öğrenme katsayıları ile uygulanarak, veri artırma ve öğrenme katsayı parametresinin başarıma etkisi de incelenmiştir. T-LeafNet, AlexNet, MobileNetV2 ve VGG16 ağlarının en yüksek sınıflandırma performansları %97,32, %98,32, %99,1 ve %99,21 olarak ölçülmüştür. Eğitim zamanı ve sınıflandırma performansları göz önünde bulundurulduğunda, önerilen T-LeafNet ağının, test edilen diğer ağlara kıyasen daha kısa süre ve az kaynakla, yüksek performans verdiği gözlemlenmiştir.

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Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Cited by 89 publications

References 47 publications

Plant Disease Recognition Model Based on Improved YOLOv5

Plant Disease Recognition Model Based on Improved YOLOv5

A Machine Intelligent Based Approach for the Classification and Analysis of Tomato Leaf Disease Images

Domates yapraklarında hastalık tespiti için önerilen hafif evrişimli sinir ağı ile önceden eğitilmiş ağların performans karşılaştırması

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