Grape Leaf Disease Detection and Classification Using Machine Learning

Huang, Zhongdong; Qin, Ally; Lu, Jingshu; Menon, Aparna; Gao, Jerry

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00150

Cited by 35 publications

(5 citation statements)

References 9 publications

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“…Zhaohua et al presented an ensemble model for grape leaf disease using VGG16, MobileNet, and AlexNet [23]. Peng et al proposed another hybrid method for grape leaf disease detection with ResNet50 and ResNet101 as feature extractors and SVM for classification [24].…”

Section: Related Workmentioning

confidence: 99%

GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

Karthik,

Menaka,

Ompirakash

et al. 2024

IEEE Access

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Grapes are a widely cultivated crop in the horticultural industry, renowned for their unique flavor and nutritional benefits. However, this crop is highly susceptible to various diseases that can cause significant reductions in yield and quality, resulting in considerable financial losses. Therefore, it is imperative to identify these diseases to effectively manage their spread. Traditionally, the identification of grape leaf diseases has relied on scientific expertise and observational skills. However, with the advent of deep learning methods, it is now feasible to recognize disease patterns from images of infected leaves. In this research, we propose a novel dual-track feature fusion network titled 'GrapeLeafNet' for detecting grape leaf disease. It employs a dual-track feature fusion approach, combining Inception-ResNet blocks with CBAM for local feature extraction and Shuffle-Transformer for global feature extraction. The first track uses Inception-ResNet blocks to represent features at multiple scales and map significant features, and CBAM captures significant spatial and channel dependencies. The second track employs Shuffle-Transformer to extract long-term dependencies and complex global features in images. The extracted features are then fused using Coordinate attention, enabling the network to capture both local and global contextual information. Experimental results on the Grape leaf disease dataset from Plant Village demonstrate the effectiveness of the proposed network, achieving an accuracy of 99.56%. INDEX TERMSGrape leaf disease, deep learning, transformer, CNN, attention I. INTRODUCTION

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Section: Related Workmentioning

confidence: 99%

GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

Karthik,

Menaka,

Ompirakash

et al. 2024

IEEE Access

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“…Moreover, infections are commonly addressed with the use of chemical compounds for preventive reasons or curable cases, or with destruction of crops to avoid further spread. This method, however, results in significant losses in crop yield, decrease in farmer's income, and ecological contamination [14][15][16], without ensuring fewer losses [17].…”

Section: Introductionmentioning

confidence: 99%

An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

Morellos,

Dolaptsis,

Tziotzios

et al. 2024

Applied Sciences

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Grapevine is a valuable and profitable crop that is susceptible to various diseases, making effective disease detection crucial for crop monitoring. This work explores the use of deep learning-based plant disease detection as an alternative to traditional methods, employing an Internet of Things approach. An edge device, a Raspberry Pi 4 equipped with an RGB camera, is utilized to detect diseases in grapevine plants. Two lightweight deep learning models, MobileNet V2 and EfficientNet B0, were trained using a transfer learning technique on commercially available online dataset, then deployed and validated on field-site in an organic winery. The models’ performance was further enhanced using semantic segmentation with the Mobile-UNet algorithm. Results were reported through a web service using FastAPI. Both models achieved high training accuracies exceeding 95%, with MobileNet V2 slightly outperforming EfficientNet B0. During validation, MobileNet V2 achieved an accuracy of 94%, compared to 92% for EfficientNet B0. In terms of IoT deployment, MobileNet V2 exhibits faster inference time (330 ms) compared to EfficientNet B0 (390 ms), making it the preferred model for online deployment.

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“…Grapes are one of the most important agricultural crops, as they represent a significant source of income for local farmers and breeders, and also play an important role in wine production. However, the achievement of high yields of grapes in these regions can be significantly limited by various diseases [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Rudenko,

Kazak,

Oleinikov

et al. 2023

Computation

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Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.

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Grape Leaf Disease Detection and Classification Using Machine Learning

Cited by 35 publications

References 9 publications

GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

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