Comparative Investigations on Tomato Leaf Disease Detection and Classification Using CNN, R-CNN, Fast R-CNN and Faster R-CNN

Priyadharshini, G.; Dolly, D. Raveena Judie

doi:10.1109/icaccs57279.2023.10112860

Cited by 21 publications

(5 citation statements)

References 17 publications

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“…The Plant Village dataset [19] has been mined for images of Tomato diseases. More than 50,000 photos collection contains a variety of crops.…”

Section: Proposed Methodologymentioning

confidence: 99%

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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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“…The Plant Village dataset [19] has been mined for images of Tomato diseases. More than 50,000 photos collection contains a variety of crops.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Images of Tomato illnesses were culled from the Plant Village collection [19][25]. The collection features over fifty thousand images of fourteen distinct agricultural products: vegetables, blueberries, raspberries, tomatoes, potatoes, grapes, apples, maize, soybeans, squash, and strawberries.…”

Section: Figure 2 Tomato Leaf Samples Datasetmentioning

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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“…Liu and Wang (2021) suggested modifying the Faster RCNN (Ren et al, 2015) framework to automatically detect beet spot diseases by changing the parameters of the CNN model. Priyadharshini and Dolly (2023) provided a comparative investigation on tomato leaf disease detection and classification using RCNN (Girshick et al, 2014), Fast RCNN (Girshick, 2015) and Faster RCNN (Ren et al, 2015). Murugeswari et al (2022) trained a model using 1500 images of healthy and diseased sugarcane leaves and deployed the model in an android application.…”

Section: Deep Learning Technics In Plant Disease Detectionmentioning

confidence: 99%

A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection

Dong,

Fuentes,

Yoon

et al. 2023

Front. Plant Sci.

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Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement restricts the model’s adaptability when encountering samples from unseen disease categories. Additionally, there is a challenge of knowledge degradation for these static learning settings, as the acquisition of new knowledge tends to overwrite the old when learning new categories. To overcome these limitations, this study introduces a novel paradigm for plant disease detection called open-world setting. Our approach can infer disease categories that have never been seen during the model training phase and gradually learn these unseen diseases through dynamic knowledge updates in the next training phase. Specifically, we utilize a well-trained unknown-aware region proposal network to generate pseudo-labels for unknown diseases during training and employ a class-agnostic classifier to enhance the recall rate for unknown diseases. Besides, we employ a sample replay strategy to maintain recognition ability for previously learned classes. Extensive experimental evaluation and ablation studies investigate the efficacy of our method in detecting old and unknown classes. Remarkably, our method demonstrates robust generalization ability even in cross-species disease detection experiments. Overall, this open-world and dynamically updated detection method shows promising potential to become the future paradigm for plant disease detection. We discuss open issues including classification and localization, and propose promising approaches to address them. We encourage further research in the community to tackle the crucial challenges in open-world plant disease detection. The code will be released at https://github.com/JiuqingDong/OWPDD.

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“…Natarajan et al [36] use Faster R-CNN deep learning model [37] to detect infected tomato 1090 images from Andhra Pradesh. Another group compared Basic R-CNN [7] , Fast R-CNN [38] , and Faster R-CNN method on their tomato leaf infection data [39] . Jiaotao built an improved YOLO model with a visual attention mechanism added to focus Images of some citrus diseases from perspective of entire tree and a close observation of leaves [49] .…”

Section: Tomato Disease Detectionmentioning

confidence: 99%

Deep learning approaches for object recognition in plant diseases: a review

Zhou,

Zhang,

et al. 2023

Intelligence & Robotics

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Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular research topic. Researchers have been experimenting with object recognition algorithms, specifically convolutional neural networks, to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

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Comparative Investigations on Tomato Leaf Disease Detection and Classification Using CNN, R-CNN, Fast R-CNN and Faster R-CNN

Cited by 21 publications

References 17 publications

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

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

A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection

Deep learning approaches for object recognition in plant diseases: a review

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