Deep Transfer Learning Models for Tomato Disease Detection

Ouhami, Maryam; Es-Saady, Youssef; Hajji, Mohamed El; Hafiane, Adel; Canals, Raphaël; Yassa, Mostafa El

doi:10.1007/978-3-030-51935-3_7

Cited by 31 publications

(12 citation statements)

References 17 publications

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“…The two branches can be symmetric or asymmetric structures and then uses bilinear pooling to achieve feature fusion. The first step is to replace the four dense blocks in DenseNet [50] with Inception structures and implement a dense connection strategy for the four Inception modules to extract multi-dimensional and multi-scale information about diseased leaves. Among them, the Inception structure contains convolutional kernels of different sizes.…”

Section: Tomato Leaf Disease Recognition Based On Dimpcnet Modelmentioning

confidence: 99%

Recognition of Tomato Leaf Diseases Based on DIMPCNET

Ding

Zhao

et al. 2023

Agronomy

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The identification of tomato leaf diseases is easily affected by complex backgrounds, small differences between different diseases, and large differences between the same diseases. Therefore, we propose a novel classification network for tomato leaf disease, the Dense Inception MobileNet-V2 parallel convolutional block attention module network (DIMPCNET). To begin, we collected a total of 1256 original images of 5 tomato leaf diseases and expanded them to 8190 using data enhancement techniques. Next, an improved bilateral filtering and threshold function (IBFTF) algorithm is designed to effectively remove noise. Then, the Dense Inception convolutional neural network module (DI) was designed to alleviate the problem of large intra-class differences and small inter-class differences. Then, a parallel convolutional block attention module (PCBAM) was added to MobileNet-V2 to reduce the impact of complex backgrounds. Finally, the experimental results show that the recognition accuracy and F1-score obtained by DIMPCNET are 94.44% and 0.9475. The loss is approximately 0.28%. This method is the most advanced and provides a new idea for the identification of crop diseases, such as tomatoes, and the development of smart agriculture.

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Section: Tomato Leaf Disease Recognition Based On Dimpcnet Modelmentioning

confidence: 99%

Recognition of Tomato Leaf Diseases Based on DIMPCNET

Ding

Zhao

et al. 2023

Agronomy

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“…The best performance was achieved after some model architectures were trained. The major goal of Maryam Ouhami et al [30]. 's research is to determine the best machine learning model for diagnosing tomato plant leaf illnesses in images using a conventional RGB input.…”

Section: Related Workmentioning

confidence: 99%

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Ramya,

Kumar

2023

Environ. Res. Commun.

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Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.

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“…Some authors have tested EfficientNet on the PlantVillage dataset [55]; the model outperformed state-ofthe-art deep learning models achieving 99.91% and 99.97% in the original and augmented datasets, respectively. Likewise, in [56], a smaller dataset of infected tomato plant leaves images was divided into different pest attacks and plant diseases. The detection method achieved an accuracy of 95.65% using the DensNet161 with transfer learning.…”

Section: Ground Imagingmentioning

confidence: 99%

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

et al. 2021

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Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.

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Deep Transfer Learning Models for Tomato Disease Detection

Cited by 31 publications

References 17 publications

Recognition of Tomato Leaf Diseases Based on DIMPCNET

Recognition of Tomato Leaf Diseases Based on DIMPCNET

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

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