Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4

Gonzalez-Huitron, Victor; León-Borges, José A.; Rodríguez-Mata, Abraham Efraim; Amábilis-Sosa, Leonel Ernesto; Ramírez-Pereda, Blenda; Rodríguez, Héctor

doi:10.1016/j.compag.2020.105951

Cited by 120 publications

(53 citation statements)

References 28 publications

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“…It helps the model converge faster compared to other networks. Thus, it is in line with research conducted by Huitron et al (2021), which detected the disease in the tomato leaves using CNN which the pre-trained model GoogLeNet and ResNet had more superior performance than other pre-trained CNN. However, since the classification of purity level only relies on visual appearance (brownish-black color, morphology, and texture) and the appearance between two mixtures (Arabica Luwak and Arabica regular) is similar, classifying all samples has only reached 89.65% accuracy.…”

Section: Resultssupporting

confidence: 87%

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2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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Section: Resultssupporting

confidence: 87%

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2022

JST

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“…Tab. 13, shows results comparison i.e., [26,[57][58][59][60] where a pre-trained VGG model has been employed for diseases orange detection [26]. The convolutional neural model has been utilized for Tomato classification and achieved 99.32% and 99.0% accuracy [57,58] respectively.…”

Section: Experiments #2 Classification Of Different Types Of Plant Diseasesmentioning

confidence: 99%

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

Amin¹,

Anjum²,

Sharif³

et al. 2022

Computers, Materials &Amp; Continua

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As they have nutritional, therapeutic, so values, plants were regarded as important and they're the main source of humankind's energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore, the proposed study presents a new framework for the recognition of fruits and vegetable diseases. This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such as You Only Look Once (YOLO)v2 and Open Exchange Neural (ONNX) model. The localization model is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model. The localized images passed as input to classify the different types of plant diseases. The classification model is constructed by ensembling the deep features learning, where features are extracted dimension of 1 × 1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input, 01 ReLU, 01 Batch-normalization, 02 fully-connected. The proposed model classifies the plant input images into associated labels with approximately 95% prediction scores that are far better as compared to current published work in this domain.

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“…In this paper, unlike the traditional research that only uses lightweight models [20][21][22][23]47], we combine pruning, distillation, and quantization methods to minimize the size of the model while ensuring accuracy. Compared with existing lightweight models, our proposed model compression method on VGGNet and AlexNet yields more competitive results (see Section 4.3 for details).…”

Section: Architectures For Plant Disease Detectionmentioning

confidence: 99%

“…Researchers in [19] deployed a lightweight neural network after knowledge distillation on an agricultural robot platform to distinguish between weeds and crops. In [20][21][22][23], authors used lightweight CNNs to identify diseased crop leaves for easier deployment on embedded devices. However, in a related study of model compression [24], a lightweight network was only one part of the solution.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Compression for Plant Disease Recognition

Wang

Ding

et al. 2021

Symmetry

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Deep neural networks (DNNs) have become the de facto standard for image recognition tasks, and their applications with respect to plant diseases have also obtained remarkable results. However, the large number of parameters and high computational complexities of these network models make them difficult to deploy on farms in remote areas. In this paper, focusing on the problems of resource constraints and plant diseases, we propose a DNN-based compression method. In order to reduce computational burden, this method uses lightweight fully connected layers to accelerate reasoning, pruning to remove redundant parameters and reduce multiply–accumulate operations, knowledge distillation instead of retraining to restore the lost accuracy, and then quantization to compress the size of the model further. After compressing the mainstream VGGNet and AlexNet models, the compressed versions are applied to the Plant Village dataset of plant disease images, and a performance comparison of the models before and after compression is obtained to verify the proposed method. The results show that the model can be compressed to 0.04 Mb with an accuracy of 97.09%. This experiment also proves the effectiveness of knowledge distillation during the pruning process, and compressed models are more efficient than prevalent lightweight models.

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Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4

Cited by 120 publications

References 28 publications

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Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

Deep Neural Network Compression for Plant Disease Recognition

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