Deep Learning Based Automatic Grape Downy Mildew Detection

Zhang, Zhao; Qiao, Yongliang; Guo, Yangyang; He, Dongjian

doi:10.3389/fpls.2022.872107

Cited by 24 publications

(11 citation statements)

References 59 publications

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“…The GMLDD model achieved a mean Average Precision (mAP) of 71.0%. Zhang et al [41] proposed a detection model called (YOLOv5 CA), which combines the YOLOv5 architecture with a coordinate attention mechanism, specifically designed for the detection of grape downy mildew disease in plant leaves. The authors trained the model using a self-generated dataset comprising 820 images.…”

Section: Comparative Studymentioning

confidence: 99%

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Tej,

Bouaafia,

Hajjaji

et al. 2024

Preprint

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Agriculture serves as a vital sector in Tunisia, supporting the nation's economy and ensuring food production. However, the detrimental impact of plant diseases on crop yield and quality presents a significant challenge for farmers. In this context, computer vision techniques have emerged as promising tools for automating disease detection processes. This paper focuses on the application of the YOLOv5 algorithm for the simultaneous detection and localization of multiple plant diseases on leaves. By using a self-generated dataset and employing techniques such as augmentation, anchor clustering, and segmentation, the study aims to enhance detection accuracy. An ablation study comparing YOLOv5s and YOLOv5x models demonstrates the superior performance of YOLOv5x, achieving a mean average precision (mAP) of 96.5%.

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Section: Comparative Studymentioning

confidence: 99%

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Tej,

Bouaafia,

Hajjaji

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhu et al (2021) proposed YOLOv3-SPP network for detection of black rot on grape leaves, applied in field environment with 86.69% precision and 82.27% recall. Zhang Z. et al (2022) proposed a YOLOv5-CA, which highlights the downy mildew disease-related visual features to achieve an mAP of 89.55%. Both methods employed YOLO for the detection of a single disease in grapes.…”

Section: Introductionmentioning

confidence: 99%

Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO

Liu,

Yu,

Geng

2024

Front. Plant Sci.

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IntroductionGrapes are prone to various diseases throughout their growth cycle, and the failure to promptly control these diseases can result in reduced production and even complete crop failure. Therefore, effective disease control is essential for maximizing grape yield. Accurate disease identification plays a crucial role in this process. In this paper, we proposed a real-time and lightweight detection model called Fusion Transformer YOLO for 4 grape diseases detection. The primary source of the dataset comprises RGB images acquired from plantations situated in North China.MethodsFirstly, we introduce a lightweight high-performance VoVNet, which utilizes ghost convolutions and learnable downsampling layer. This backbone is further improved by integrating effective squeeze and excitation blocks and residual connections to the OSA module. These enhancements contribute to improved detection accuracy while maintaining a lightweight network. Secondly, an improved dual-flow PAN+FPN structure with Real-time Transformer is adopted in the neck component, by incorporating 2D position embedding and a single-scale Transformer Encoder into the last feature map. This modification enables real-time performance and improved accuracy in detecting small targets. Finally, we adopt the Decoupled Head based on the improved Task Aligned Predictor in the head component, which balances accuracy and speed.ResultsExperimental results demonstrate that FTR-YOLO achieves the high performance across various evaluation metrics, with a mean Average Precision (mAP) of 90.67%, a Frames Per Second (FPS) of 44, and a parameter size of 24.5M.ConclusionThe FTR-YOLO presented in this paper provides a real-time and lightweight solution for the detection of grape diseases. This model effectively assists farmers in detecting grape diseases.

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“…This technique may be combined into improved livestock production by predicting reproductive patterns, diagnosing eating problems, animal behavior using ML models, etc. [25,26].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Yield Prediction in Precision Agriculture

N.,

2024

Agricultural Sciences

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India’s agriculture industry is crucial to its economic growth and one of its most fundamental endeavors. Regarding a country’s economic prosperity, agriculture is among the most significant factors contributing to the happiness and well-being of its citizens. To improve agricultural output, “smart agriculture,” or the use of technology, strives for more accurate disease control, irrigation, and yield prediction. Precision agriculture is applying big data analytics and the Internet of Things to the farming industry. Agricultural production will increase dramatically as a result of this. The Internet of Things (IoT) and massive amounts of data are used in precision agriculture to improve crop quality and yields. In this research, we use the grape plants and their associated factors (temperature, humidity, rainfall, pH, sun irradiance, etc.) from the Smart Agriculture dataset to develop an N-stage CNN. In this work, we use machine learning approaches for irrigation scheduling and the DoubleGAN methodology for disease diagnosis in plants. This effort aims to create an N-stage CNN model that will significantly boost agricultural output by enhancing grape plant yield. The yield prediction is quite accurate since we considered practically all necessary characteristics and photos for plant development, including irrigation schedule and disease detection.

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Deep Learning Based Automatic Grape Downy Mildew Detection

Cited by 24 publications

References 59 publications

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

An Improved YOLOv5 for Accurate Detection and Localization of Tomato and Pepper Leaf Diseases

Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO

Big Data Analytics for Yield Prediction in Precision Agriculture

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