An Efficient Hybrid CNN Classification Model for Tomato Crop Disease

Sanida, Maria Vasiliki; Sanida, Theodora; Sideris, Argyrios; Dasygenis, Minas

doi:10.3390/technologies11010010

Cited by 25 publications

(11 citation statements)

References 31 publications

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“…The major objective of FGVC in the agriculture sector is to accurately identify informative regions in the picture. This has been a problem in previous computational work [12,13], for example where it was found that the model had trouble zeroing in on informative regions, leading to poor classification accuracy. When it comes to agriculture's sustainability, plant diseases have historically been one of the biggest obstacles.…”

Section: Figure 1 Tomato Leaf Samples With Various Diseasementioning

confidence: 98%

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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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Section: Figure 1 Tomato Leaf Samples With Various Diseasementioning

confidence: 98%

“…However, farmers and gardeners aren't always successful in achieving optimal plant development [12]. Sometimes the tomatoes will not mature at all, and other times they will ripen but have unsightly black patches on the bottom that look sick.…”

Section: Introductionmentioning

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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“…The validation set is utilised to estimate the network's effectiveness on unknown data during training to enhance the network's performance. The test set provides a final, objective assessment of the network's performance after training on unseen data [31].…”

Section: Split Collectionmentioning

confidence: 99%

A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

et al. 2023

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The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective.

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“…Plant diseases present a crucial obstacle to the growth of agriculture in every country, resulting in significant yearly financial losses ( Mitra, 2021 ). Plant disease detection has developed into a substantial area of study in pattern recognition and contemporary agricultural development due to developments in machine learning technology ( Roy and Bhaduri, 2021 ; Albattah et al., 2022 ; Sanida et al., 2023 ). Early plant disease identification approaches used a support vector machine (SVM) ( Rahman et al., 2023 ; Thangavel et al., 2023 ), artificial neural network (ANN) ( Attallah, 2023 ), and SVM method for disease diagnosis under segmented plant disease spots.…”

Section: Introductionmentioning

confidence: 99%

Detection and identification of plant leaf diseases using YOLOv4

Aldakheel,

Zakariah,

Alabdalall

2024

Front. Plant Sci.

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Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers’ dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model’s resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study’s results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.

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An Efficient Hybrid CNN Classification Model for Tomato Crop Disease

Cited by 25 publications

References 31 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 Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

Detection and identification of plant leaf diseases using YOLOv4

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