Automated detection of downy mildew and powdery mildew symptoms for vineyard disease management
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Ghiani, Luca
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Univ Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
Interdept Ctr IA INNOVAT AGR Loc Surigheddu, SS 127 bis,Kim 28,500, Alghero Ss 07041, ItalyUniv Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
Ghiani, Luca
[1
,3
]
Serra, Salvatorica
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Univ Sassari, Dept Agr Sci, Viale Italia 39a, I-07100 Sassari, Italy
Interdept Ctr IA INNOVAT AGR Loc Surigheddu, SS 127 bis,Kim 28,500, Alghero Ss 07041, ItalyUniv Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
Serra, Salvatorica
[2
,3
]
Sassu, Alberto
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Univ Sassari, Dept Agr Sci, Viale Italia 39a, I-07100 Sassari, ItalyUniv Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
Sassu, Alberto
[2
]
Deidda, Alessandro
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Univ Sassari, Dept Agr Sci, Viale Italia 39a, I-07100 Sassari, ItalyUniv Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
Deidda, Alessandro
[2
]
Deidda, Antonio
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Univ Sassari, Dept Agr Sci, Viale Italia 39a, I-07100 Sassari, ItalyUniv Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
Deidda, Antonio
[2
]
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Gambella, Filippo
[2
,3
]
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[1] Univ Sassari, Dept Biomed Sci, Viale San Pietro 43-B, I-07100 Sassari, Italy
This work focuses on developing an automated system for detecting downy mildew and powdery mildew symptoms in grapevines, with particular attention to the role of data partitioning and dataset diversity in ensuring reliable model performance. Leveraging deep learning techniques, specifically the YOLO (You Only Look Once) object detection model, we aimed to provide a robust tool for disease detection, which is crucial for optimizing vineyard management, increasing crop yield, and promoting sustainable agricultural practices. Over two years, we collected and expertly annotated a large dataset of images depicting downy and powdery mildew symptoms in field conditions. The YOLO model was trained and validated on this dataset, achieving a mean Average Precision (mAP) of 0.730, demonstrating good detection accuracy. A key contribution of this study is the emphasis on the importance of proper data partitioning strategies, showing that random image partitioning can lead to an overestimation of model performance. Our findings underscore that true improvements in detection accuracy are driven not merely by increasing the number of images but by enhancing the diversity of the dataset, particularly for the areas, seasons, growth stages, and conditions in which the images are captured. This approach ensures a more realistic assessment of the system's performance, critical for deploying such systems in practical, real-world agricultural scenarios. The results highlight the potential of deep learning models to enhance vineyard management through a reliable and efficient detection of diseases in real-world conditions.
机构:
Univ La Rioja, Televitis Res Grp, Logrono 26006, Spain
Univers La Rioja, Inst Grapevine & Wine Sci, Consejo Super Invest Cient, Gobierno La Rioja, Logrono 26007, SpainUniv La Rioja, Televitis Res Grp, Logrono 26006, Spain
Hernandez, Ines
Gutierrez, Salvador
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机构:
Univ Granada UGR, Andalusian Res Inst Data Sci & Computat Intelligen, Dept Comp Sci & Artificial Intelligence DECSAI, Granada 18014, SpainUniv La Rioja, Televitis Res Grp, Logrono 26006, Spain
Gutierrez, Salvador
Tardaguila, Javier
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机构:
Univ La Rioja, Televitis Res Grp, Logrono 26006, Spain
Univers La Rioja, Inst Grapevine & Wine Sci, Consejo Super Invest Cient, Gobierno La Rioja, Logrono 26007, SpainUniv La Rioja, Televitis Res Grp, Logrono 26006, Spain
机构:
Univ Santiago Compostela, Fac Pharm, Dept Bot, E-15782 Santiago De Compostela, SpainUniv Vigo, Fac Sci, Dept Plant Biol & Soil Sci, E-32004 Orense, Spain
机构:
Univ La Rioja, Televitis Res Grp, Logrono 26006, Spain
Univers La Rioja, Inst Grapevine & Wine Sci, Consejo Super Invest Cient, Gobierno La Rioja, Logrono 26007, SpainUniv La Rioja, Televitis Res Grp, Logrono 26006, Spain
Hernandez, Ines
Gutierrez, Salvador
论文数: 0引用数: 0
h-index: 0
机构:
Univ Granada UGR, Andalusian Res Inst Data Sci & Computat Intelligen, Dept Comp Sci & Artificial Intelligence DECSAI, Granada 18014, SpainUniv La Rioja, Televitis Res Grp, Logrono 26006, Spain
Gutierrez, Salvador
Tardaguila, Javier
论文数: 0引用数: 0
h-index: 0
机构:
Univ La Rioja, Televitis Res Grp, Logrono 26006, Spain
Univers La Rioja, Inst Grapevine & Wine Sci, Consejo Super Invest Cient, Gobierno La Rioja, Logrono 26007, SpainUniv La Rioja, Televitis Res Grp, Logrono 26006, Spain
机构:
Univ Santiago Compostela, Fac Pharm, Dept Bot, E-15782 Santiago De Compostela, SpainUniv Vigo, Fac Sci, Dept Plant Biol & Soil Sci, E-32004 Orense, Spain