Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks

Saeed, Alaa I.; Abdelaziz, Ahmed; Mossad, Amr; Abdelhamid, Mahmoud A.; Alkhaled, Alfadhl Yahya; Mayhoub, Muhammad

doi:10.3390/agriculture13010139

Cited by 33 publications

(16 citation statements)

References 38 publications

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“…The adapted technique was trained and validated using the Plant-Village database, with the obtained results achieving an accuracy of 99.51% for 3 tomato disease classes, 98.65% for 5 tomato disease classes, and 97.11% for 10 tomato classes. A smart approach has been proposed in [12] using pre-trained Inception Version 3 and ResNet Version 2 networks to classify tomato diseases. The most important accuracy results obtained were 99.22% with specific configurations for the pre-trained models Inception V3 and ResNet V2 with a 50% and 15% dropout rate respectively.…”

Section: Related Workmentioning

confidence: 99%

An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

Kebir,

Berrhail,

Didi

2024

Braz. J. Technol.

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In this paper, an efficient methodology for detecting and classifying tomato diseases using a Convolutional Neural Network (CNN) deep learning network is presented as an efficient aided tool to classify different tomato diseases based on their leaf appearence, where plant diseases and insects are considered as a main challenges for farmers to overcome. The proposed methodology structure is based on 20 layers using convolution, Maxpooling, Batch normalization and ReLU process as main operations in the adopted architecture. The obtained results using Plant Village database show that our proposed methodology outclasses the best recent methods of tomato diseases detection and classification with scores of 97.3% 96.8% 97.0% and 97.5% for precision, Recall, F1-Score and accuracy coefficient criteria respectively. Our methodology of tomato diseases detection and classification is found as effective, accurate and aided diagnostic tool that aims to aid farmers to make a precise plant treatments and enhancing productivity while promoting environmental sustainability.

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Section: Related Workmentioning

confidence: 99%

An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

Kebir,

Berrhail,

Didi

2024

Braz. J. Technol.

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“…Batch normalization layer to speed up the training of deep neural networks, batch normalization attempts to minimize internal covariate shift. This is done by fixing the means and variances of the input layer layers during a normalization stage (Saeed et al, 2023). The following equations describe the calculation for BN operation: 1) Determine the mean of mini-batch (ℬ)…”

Section: Transfer Learning (Pre-trained Models)mentioning

confidence: 99%

Classification of Dates Quality Using Deep Learning Technology Based on Captured Images

ElHelew,

Abo-Bbakr,

Zayan

et al. 2024

Misr Journal of Agricultural Engineering

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Dates are a common fruit in many Middle Eastern and African nations and have religious and cultural value. one of the key elements in judging the quality of dates is sorting according to their health state. Combining rejected dates with accepted ones causes significant economic losses in both storage and exportation. Despite being a crucial stage for obtaining highquality dates and reducing losses, this sorting process is still conducted using traditional methods. Thus, this study aims to classify date fruit quality (accepted or rejected) with machine learning technology to reduce cost, time, and improve the quality of final product. In this study, several Convolutional Neural Network architectures VGG19) were used to classify three varieties of date fruit (Mejdool, Saiedi, El-Wadi). These varieties were classified into accepted and rejected samples to build the dataset im-ages. An Arduino Automatic mobile camera shutter controller captured the dataset images. In addition to the Kaggle dataset which was added to the accepted images. The total dataset consisted of 5,945 images, comprising 3,142 accepted images and 2,803 rejected images. By comparing the results of different architectures, Inception-ResNet-v2 demonstrated the best performance, achieving an accuracy of 98.99% and a loss of 0.0344. Therefore, it can be concluded that the Inception-ResNet-v2 model could be utilized to develop a suitable computer vision system, thereby enhancing the date sorting process and facilitating the packaging of high-quality dates.

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“…Using a pre-trained VGGNet model, they achieved an accuracy of 91.83% for recognizing the diseases available in the PlantVillage dataset, while the accuracy of identifying examples within the self-collected database was 92.00%. Saeed et al [25] used two pretrained CNNs, InceptionV3 and InceptionResNetV2, which were trained on 5225 images from the PlantVillage dataset [21] to classify healthy and unhealthy tomato leaves. They investigated the impact of using different dropout rates and concluded that the best results were achieved using InceptionV3 with a 50% dropout rate and InceptionResNetV2 with a 15% dropout rate, with an accuracy of 99.22%.…”

Section: Related Workmentioning

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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Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks

Cited by 33 publications

References 38 publications

An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

Classification of Dates Quality Using Deep Learning Technology Based on Captured Images

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

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