Analysis of Different CNN Architectures for Tomato Leaf Disease Classification

Gehlot, Mamta; Saini, M. L.

doi:10.1109/icraie51050.2020.9358279

Cited by 28 publications

(6 citation statements)

References 17 publications

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“…Many studies 13‐15 have used disease leaf images of tomatoes from the PlantVillage data set for tomato disease‐related studies, which is sufficient to confirm the reliability of this dataset.…”

Section: Methodsmentioning

confidence: 98%

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A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Jing

Qiao

et al. 2023

J Sci Food Agric

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BACKGROUNDTomato is one of the most important vegetables in the world. Timely and accurate identification of tomato disease is a critical way to ensure the quality and yield of tomato production. The convolutional neural network is a crucial means of disease identification. However, this method requires manual annotation of a large amount of image data, which wastes the human cost of scientific research.RESULTSTo simplify the process of disease image labeling and improve the accuracy of tomato disease recognition and the balance of various disease recognition effects, a BC‐YOLOv5 tomato disease recognition method is proposed to identify healthy growth and nine types of diseased tomato leaves. In the present study, the YOLOv5 model is improved by designing an automatic tomato leaf image labeling algorithm, using the weighted bi‐directional feature pyramid network to change the Neck structure, adding the convolution block attention module, and changing the input channel of the detection layer. Experiments show that the BC‐YOLOv5 method has an excellent image annotation effect on tomato leaves, with a pass rate exceeding 95%. Furthermore, compared with existing models, the performance indices of BC‐YOLOv5 to identify tomato diseases are the best.CONCLUSIONBC‐YOLOv5 realizes the automatic labeling of tomato leaf images before the start of training. This method not only identifies nine common tomato diseases, but also improve the accuracy of disease identification and have a more balanced identification effect on various diseases. It provides a reliable method for the identification of tomato disease. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 98%

“…The value smaller indicates that the model has a more balanced identification effect on various diseases. The calculation formula of the variance is shown in Eqn (15).…”

Section: Evaluation Indicesmentioning

confidence: 99%

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Jing

Qiao

et al. 2023

J Sci Food Agric

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“…The remarkable accuracy of 99.60% has been achieved by Ulutaş and Aslantaş's (2023) ensemble CNN model for the identification of tomato leaf diseases [2]. Gehlot and Saini (2020) examined many CNN architectures for tomato leaf disease categorization and found that DenseNet-121, VGG16, and ResNet-101 performed well with comparable accuracy, precision, recall, and F1-score [3]. For the Raspberry Pi 4 [4], Gonzalez-Huitron et al ( 2021) developed lightweight CNN architectures that were subsequently utilised to construct the models for disease detection in tomato leaves.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

Akash Saxena

2024

jes

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This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.

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“…Among these architectures, AlexNet generates the highest accuracy in feature extraction with 93.4%. Gehlot and Saini [114] The architectures of CNNs have been classified gradually with the increasing number of convolutional layers, namely LeNet, AlexNet, Visual Geometri Group 16 (VGG16), VGG19, ResNet, GoogLeNet ResNext, DenseNet and You Only Look Once (YOLO). The differences between these architectures are the number of layers, non-linearity function and the pooling type used [110].…”

Section: Deep Learning For Image Annotationmentioning

confidence: 99%

“…Among these architectures, AlexNet generates the highest accuracy in feature extraction with 93.4%. Gehlot and Saini [114] Figure 5 presents the details on the image annotation and its deep learning approach technique. Low-level features are used to represent images in image classification and retrieval.…”

Section: Deep Learning For Image Annotationmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

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Analysis of Different CNN Architectures for Tomato Leaf Disease Classification

Cited by 28 publications

References 17 publications

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

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