Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network

Gnanavel, S.; Sathianesan, Godfrey Winster; Murugan, Vetri Selvan; Reddy, A. Jayaram; Jayagopal, Prabhu; Elsisi, Mahmoud

doi:10.3390/electronics11213618

Cited by 63 publications

(13 citation statements)

References 27 publications

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“…CNNs are commonly used algorithms for the development of automated systems for identifying plant diseases by detecting the symptoms on leaves [22]. As a method of deep machine learning, CNN represents the most promising solution to this problem due to its precise approach of extracting visual features without the use of segmentation [32][33][34]. It usually consists of convolution layers with different values of the kernel, which can be in parallel and activation functions, followed by pooling layers and fully connected layers [22,[35][36][37].…”

Section: Structure Of Transfer Deep Learning Network For Tomato Leaf ...mentioning

confidence: 99%

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

Radočaj,

Martinović

2024

BDCC

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Due to the projected increase in food production by 70% in 2050, crops should be additionally protected from diseases and pests to ensure a sufficient food supply. Transfer deep learning approaches provide a more efficient solution than traditional methods, which are labor-intensive and struggle to effectively monitor large areas, leading to delayed disease detection. This study proposed a versatile module based on the Inception module, Mish activation function, and Batch normalization (IncMB) as a part of deep neural networks. A convolutional neural network (CNN) with transfer learning was used as the base for evaluated approaches for tomato disease detection: (1) CNNs, (2) CNNs with a support vector machine (SVM), and (3) CNNs with the proposed IncMB module. In the experiment, the public dataset PlantVillage was used, containing images of six different tomato leaf diseases. The best results were achieved by the pre-trained InceptionV3 network, which contains an IncMB module with an accuracy of 97.78%. In three out of four cases, the highest accuracy was achieved by networks containing the proposed IncMB module in comparison to evaluated CNNs. The proposed IncMB module represented an improvement in the early detection of plant diseases, providing a basis for timely leaf disease detection.

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Section: Structure Of Transfer Deep Learning Network For Tomato Leaf ...mentioning

confidence: 99%

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

Radočaj,

Martinović

2024

BDCC

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“…The trained models demonstrated high accuracy, with the best testing precision reaching 95.71%. Sakkarvarthi, Gnanavel, et al in the article [22] proposes a deep-learning-based agricultural disease detection technique, employing a CNN approach in order to detect and classify diseases. The model, consisting of a pair of convolutional and pooling layers, exceeded the performance of the pre-trained InceptionV3, ResNet 152, and VGG19 models, achieving 98% training accuracy and 88.17% testing accuracy.…”

Section: Related Workmentioning

confidence: 99%

Tomato Disease Recognition: Advancing Accuracy Through Xception and Bilinear Pooling Fusion

Vo,

Thien,

Mui

2023

IJACSA

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Accurate detection and classification of tomato diseases are essential for effective disease management and maintaining agricultural productivity. This paper presents a novel approach to tomato disease recognition that combines Xception, a pre-trained convolutional neural network (CNN), with bilinear pooling to advance accuracy. The proposed model consists of two parallel Xception-based CNNs that independently process input tomato images. Bilinear pooling is applied to combine the feature maps generated by the two CNNs, capturing intricate interactions between different image regions. This fusion of Xception and bilinear pooling results in a comprehensive representation of tomato diseases, leading to improved recognition performance. Extensive experiments were conducted on a diverse dataset of annotated tomato disease images to evaluate the effectiveness of the suggested approach. The model achieved a remarkable test accuracy of 98.7%, surpassing conventional CNN approaches. This high accuracy demonstrates the efficacy of the integrated Xception and bilinear pooling model in accurately identifying and classifying tomato diseases. The implications of this research are significant for automated tomato disease recognition systems, enabling timely and precise disease diagnosis. The model's exceptional accuracy empowers farmers and agricultural practitioners to implement targeted disease management strategies, minimizing crop losses and optimizing yields.

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“…In structural engineering, AI and ML improve design by overcoming traditional model limitations, employing pattern recognition (PR) and deep learning (DL) [ 4 ]. Agriculture benefits from AI and ML through real-time detection of plant diseases [ 5 ], such as using deep-learning methods to detect and categorize leaf diseases in tomatoes [ 6 ], and even in medical analysis for diabetes diagnosis [ 7 ]. Additionally, AI has seamlessly integrated with IoT systems, enhancing efficiency and security in various industries [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal control of cooling management system for energy conservation in smart home with ANNs-PSO data analytics microservice platform

Sirisumrannukul,

Intaraumnauy,

Piamvilai

2024

Heliyon

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Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network

Cited by 63 publications

References 27 publications

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

Tomato Disease Recognition: Advancing Accuracy Through Xception and Bilinear Pooling Fusion

Optimal control of cooling management system for energy conservation in smart home with ANNs-PSO data analytics microservice platform

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