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

Batuhan Asiroglu; Aydın Kağan Türkoğlu; Fatih Sinan Bir; Sibel Senan

doi:10.15832/ankutbd.1898342

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

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.

Keywords

Supporting Institution

the study is supported by the Scientific and Technological Research Council of Turkey under the 1509-TUBITAK International Industrial R&D Projects Support Program, Project Number: 9210046

Project Number

the study is supported by the Scientific and Technological Research Council of Turkey under the 1509-TUBITAK International Industrial R&D Projects Support Program, Project Number: 9210046

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Details

Primary Language

English

Subjects

Modelling and Simulation, Planning and Decision Making, Environmental Assessment and Monitoring, Agricultural Biotechnology Diagnostics

Journal Section

Research Article

Authors

Batuhan Asiroglu
0000-0003-0767-6348
Türkiye

Aydın Kağan Türkoğlu
0009-0001-8383-8807
Türkiye

Fatih Sinan Bir This is me
0009-0008-7984-8413
Türkiye

Sibel Senan ^*
0000-0001-6773-0428
Türkiye

Publication Date

October 12, 2026

Submission Date

February 26, 2026

Acceptance Date

May 13, 2026

Published in Issue

Year 2026 Volume: 32 Number: 4

DOI

https://doi.org/10.15832/ankutbd.1898342

IZ

https://izlik.org/JA62ZZ38HW

Cite

RIS / Bibtex

APA

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. Journal of Agricultural Sciences, 32(4), 1030-1050. https://doi.org/10.15832/ankutbd.1898342

AMA

1.Asiroglu B, Türkoğlu AK, Bir FS, Senan S. Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery. J Agr Sci-Tarim Bili. 2026;32(4):1030-1050. doi:10.15832/ankutbd.1898342

Chicago

Asiroglu, Batuhan, Aydın Kağan Türkoğlu, Fatih Sinan Bir, and Sibel Senan. 2026. “Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery”. Journal of Agricultural Sciences 32 (4): 1030-50. https://doi.org/10.15832/ankutbd.1898342.

EndNote

Asiroglu B, Türkoğlu AK, Bir FS, Senan S (October 1, 2026) Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery. Journal of Agricultural Sciences 32 4 1030–1050.

IEEE

[1]B. Asiroglu, A. K. Türkoğlu, F. S. Bir, and S. Senan, “Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery”, J Agr Sci-Tarim Bili, vol. 32, no. 4, pp. 1030–1050, Oct. 2026, doi: 10.15832/ankutbd.1898342.

ISNAD

Asiroglu, Batuhan - Türkoğlu, Aydın Kağan - Bir, Fatih Sinan - Senan, Sibel. “Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery”. Journal of Agricultural Sciences 32/4 (October 1, 2026): 1030-1050. https://doi.org/10.15832/ankutbd.1898342.

JAMA

1.Asiroglu B, Türkoğlu AK, Bir FS, Senan S. Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery. J Agr Sci-Tarim Bili. 2026;32:1030–1050.

MLA

Asiroglu, Batuhan, et al. “Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery”. Journal of Agricultural Sciences, vol. 32, no. 4, Oct. 2026, pp. 1030-5, doi:10.15832/ankutbd.1898342.

Vancouver

1.Batuhan Asiroglu, Aydın Kağan Türkoğlu, Fatih Sinan Bir, Sibel Senan. Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery. J Agr Sci-Tarim Bili. 2026 Oct. 1;32(4):1030-5. doi:10.15832/ankutbd.1898342