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Journals / Tarım Bilimleri Dergisi / 2026 / Volume: 32 Issue: 4

Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery

Batuhan Asiroglu(ISTANBUL UNIVERSITY-CERRAHPASA)0000-0003-0767-6348
Aydın Kağan Türkoğlu(YILDIZ TECHNICAL UNIVERSITY)0009-0001-8383-8807
Fatih Sinan Bir(B Tech Analytics Technology Inc.)0009-0008-7984-8413
Sibel Senan(ISTANBUL UNIVERSITY-CERRAHPASA)0000-0001-6773-0428
Pages
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Publication Date
2026-10-12
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses.
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Key Findings & Data:Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system.
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Conclusion & Impact:The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.
🔬Design:Modeling & Simulation
📍Scope:2023 Verileri
⚙️Variables:Apple Scab · Grape Vine Mildew · Plant Disease Detection

Abstract

Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.

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Cite This Article

Asiroglu, B., Türkoğlu, A. K., Bir, F. S., Senan, S. (2026). Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery. Tarım Bilimleri Dergisi, Volume: 32 Issue: 4. https://doi.org/10.15832/ankutbd.1898342

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