Tomato Disease Recognition Using a Compact Convolutional Neural Network

Özbilge, Emre; Ulukök, Mehtap Köse; Toygar, Önsen; Ozbilge, Ebru

doi:10.1109/access.2022.3192428

Cited by 35 publications

(13 citation statements)

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“…Many more CNN classification architectures like AlexNet and SqueezeNet highlighted some of the good parameters for a detailed study. In [10] a compact CNN is proposed by the authors for tomato leaf disease identification involving six layers network. Another new model based on CNN Architecture was developed with Adam optimizer and with the help of Image Augmentation an accuracy of 96.55% was achieved.…”

Section: Related Workmentioning

confidence: 99%

Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet

et al. 2023

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The advancement of Deep Learning and Computer Vision in the field of agriculture has been found to be an effective tool in detecting harmful plant diseases. Classification and detection of healthy and diseased crops play a very crucial role in determining the rate and quality of production. Thus the present work highlights a wellproposed novel method of detecting Tomato leaf diseases using Deep Neural Networks to strengthen agro-based industries. The present novel framework is utilized with a combination of classical Machine Learning model Principal Component Analysis (PCA) and a customized Deep Neural Network which has been named as PCA DeepNet. The hybridized framework also consists of Generative Adversarial Network (GAN) for obtaining a good mixture of datasets. The detection is carried out using the Faster Region-Based Convolutional Neural Network (F-RCNN). The overall work generated a classification accuracy of 99.60% with an average precision of 98.55%; giving a promising Intersection over Union (IOU) score of 0.95 in detection. Thus the presented work outperforms any other reported state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet

et al. 2023

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“…TLD categorization was suggested by Ozbılge et al [ 43 ] as an alternative to the well-known pre-trained knowledge-transferred ImageNet deep-network model and the compact deep-neural-network design with only six layers. The model’s performance on the PVdataset was tested using a number of statistical methods, and an accuracy of 99.70% was achieved.…”

Section: Related Workmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

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Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

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“… Benchmark against other models [ 27 , 47 , 48 , 49 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]. …”

Section: Figurementioning

confidence: 99%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

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Tomato Disease Recognition Using a Compact Convolutional Neural Network

Cited by 35 publications

References 35 publications

Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet

Detection of Tomato Leaf Diseases for Agro-Based Industries Using Novel PCA DeepNet

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

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