Design a mobile application to detect tomato plant diseases based on deep learning

Abdulla, Marwa; Marhoon, Ali

doi:10.11591/eei.v11i5.3918

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The problem of plant disease classification based on images plays a significant role in real-life scenarios, particularly in contributing to addressing issues related to the quality and quantity of agricultural produce. The results of research on leaf [10] plant village 19 75 0.9718 [11] plant village tomato leaf 10 14500 0.981 [12] plant village 30 53200 0.9843 [16] plantvillage 38 163,000 0.9014 [13] tomato leaf disease 7 735 0.96735 [17] Plantvillage 7 11,165 0.9571 [18] Plantvillage 14 3,000 0.98 [23] Plantvillage 14 54,305 0.9922 [24] Plantvillage 6 6,594 0.9635 [14] tomato leaf disease 8 10000 0.926 [15] plantvillage In the future, we will build new models or combine from many different models to further improve the accuracy of this problem.…”

Section: Discussionmentioning

confidence: 99%

“…They evaluated various models, such as EfficientNetB0, ResNext-50-32x4d, and MobileNet-V2, with ResNext-50-32x4d emerging as the top performer, achieving an impressive accuracy rate of 90.14%. In the paper cited as [17], the authors introduce a novel approach for classifying seven distinct types of tomato diseases employing Deep Learning models. Their models were trained on an extensive dataset comprising 10,448 images, and the results were striking.…”

Section: Related Workmentioning

confidence: 99%

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An Approach for Classification of Diseases on Leaves

2023

IJACSA

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In recent years, significant advancements have been made in the realm of plant disease classification, with a particular focus on leveraging the capabilities of deep learning techniques. This study delves into the utilization of renowned Convolutional Neural Network (CNN) models, including EfficientNetB5, Mo-bileNet, ResNet50, InceptionV3, and VGG16, for the purpose of plant disease classification. The core methodology involves employing transfer learning, wherein these established CNN models are employed as a foundation and subsequently finetuned using a publicly accessible plant disease dataset. The study also compared the results with some deep learning models and with state-of-the-art. Among the tested CNNs, EfficientNetB5 has shown the best performance. EfficientNetB5 has outperformed another model and obtained 99.2% classification accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Approach for Classification of Diseases on Leaves

2023

IJACSA

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“…Several models, including EfficientNet-B0, ResNext-50-32x4d, and MobileNet-V2, were tested, and ResNext-50-32x4d achieved the highest accuracy of 90.14%. The paper in [21] presents an approach for classifying seven different types of tomato illnesses using DL models trained on a dataset of 10,448 images. The trained models demonstrated high accuracy, with the best testing precision reaching 95.71%.…”

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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“…Atole and Park [13] carried out a transfer learning on diseases in rice. Abdulla and Marhoon [14] compared several types of transfer learning for disease classification in tomato leaves. Some of these studies indicate that the system's accuracy affects not only the model that is applied but also the dataset used and the parameters that are applied.…”

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confidence: 99%

Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Tempola,

Wardoyo,

Musdholifah

et al. 2024

Bulletin EEI

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This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.

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Design a mobile application to detect tomato plant diseases based on deep learning

Cited by 4 publications

References 22 publications

An Approach for Classification of Diseases on Leaves

An Approach for Classification of Diseases on Leaves

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

Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

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