Cargando…

Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms

Sooty mold is a common disease found in citrus plants and is characterized by black fungi growth on fruits, leaves, and branches. This mold reduces the plant’s ability to carry out photosynthesis. In small leaves, it is very difficult to detect sooty mold at the early stages. Deep learning-based ima...

Descripción completa

Detalles Bibliográficos
Autores principales: Apacionado, Bryan Vivas, Ahamed, Tofael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610784/
https://www.ncbi.nlm.nih.gov/pubmed/37896610
http://dx.doi.org/10.3390/s23208519
_version_ 1785128338038194176
author Apacionado, Bryan Vivas
Ahamed, Tofael
author_facet Apacionado, Bryan Vivas
Ahamed, Tofael
author_sort Apacionado, Bryan Vivas
collection PubMed
description Sooty mold is a common disease found in citrus plants and is characterized by black fungi growth on fruits, leaves, and branches. This mold reduces the plant’s ability to carry out photosynthesis. In small leaves, it is very difficult to detect sooty mold at the early stages. Deep learning-based image recognition techniques have the potential to identify and diagnose pest damage and diseases such as sooty mold. Recent studies used advanced and expensive hyperspectral or multispectral cameras attached to UAVs to examine the canopy of the plants and mid-range cameras to capture close-up infected leaf images. To bridge the gap on capturing canopy level images using affordable camera sensors, this study used a low-cost home surveillance camera to monitor and detect sooty mold infection on citrus canopy combined with deep learning algorithms. To overcome the challenges posed by varying light conditions, the main reason for using specialized cameras, images were collected at night, utilizing the camera’s built-in night vision feature. A total of 4200 sliced night-captured images were used for training, 200 for validation, and 100 for testing, employed on the YOLOv5m, YOLOv7, and CenterNet models for comparison. The results showed that YOLOv7 was the most accurate in detecting sooty molds at night, with 74.4% mAP compared to YOLOv5m (72%) and CenterNet (70.3%). The models were also tested using preprocessed (unsliced) night images and day-captured sliced and unsliced images. The testing on preprocessed (unsliced) night images demonstrated the same trend as the training results, with YOLOv7 performing best compared to YOLOv5m and CenterNet. In contrast, testing on the day-captured images had underwhelming outcomes for both sliced and unsliced images. In general, YOLOv7 performed best in detecting sooty mold infections at night on citrus canopy and showed promising potential in real-time orchard disease monitoring and detection. Moreover, this study demonstrated that utilizing a cost-effective surveillance camera and deep learning algorithms can accurately detect sooty molds at night, enabling growers to effectively monitor and identify occurrences of the disease at the canopy level.
format Online
Article
Text
id pubmed-10610784
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106107842023-10-28 Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms Apacionado, Bryan Vivas Ahamed, Tofael Sensors (Basel) Article Sooty mold is a common disease found in citrus plants and is characterized by black fungi growth on fruits, leaves, and branches. This mold reduces the plant’s ability to carry out photosynthesis. In small leaves, it is very difficult to detect sooty mold at the early stages. Deep learning-based image recognition techniques have the potential to identify and diagnose pest damage and diseases such as sooty mold. Recent studies used advanced and expensive hyperspectral or multispectral cameras attached to UAVs to examine the canopy of the plants and mid-range cameras to capture close-up infected leaf images. To bridge the gap on capturing canopy level images using affordable camera sensors, this study used a low-cost home surveillance camera to monitor and detect sooty mold infection on citrus canopy combined with deep learning algorithms. To overcome the challenges posed by varying light conditions, the main reason for using specialized cameras, images were collected at night, utilizing the camera’s built-in night vision feature. A total of 4200 sliced night-captured images were used for training, 200 for validation, and 100 for testing, employed on the YOLOv5m, YOLOv7, and CenterNet models for comparison. The results showed that YOLOv7 was the most accurate in detecting sooty molds at night, with 74.4% mAP compared to YOLOv5m (72%) and CenterNet (70.3%). The models were also tested using preprocessed (unsliced) night images and day-captured sliced and unsliced images. The testing on preprocessed (unsliced) night images demonstrated the same trend as the training results, with YOLOv7 performing best compared to YOLOv5m and CenterNet. In contrast, testing on the day-captured images had underwhelming outcomes for both sliced and unsliced images. In general, YOLOv7 performed best in detecting sooty mold infections at night on citrus canopy and showed promising potential in real-time orchard disease monitoring and detection. Moreover, this study demonstrated that utilizing a cost-effective surveillance camera and deep learning algorithms can accurately detect sooty molds at night, enabling growers to effectively monitor and identify occurrences of the disease at the canopy level. MDPI 2023-10-17 /pmc/articles/PMC10610784/ /pubmed/37896610 http://dx.doi.org/10.3390/s23208519 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Apacionado, Bryan Vivas
Ahamed, Tofael
Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
title Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
title_full Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
title_fullStr Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
title_full_unstemmed Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
title_short Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
title_sort sooty mold detection on citrus tree canopy using deep learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610784/
https://www.ncbi.nlm.nih.gov/pubmed/37896610
http://dx.doi.org/10.3390/s23208519
work_keys_str_mv AT apacionadobryanvivas sootymolddetectiononcitrustreecanopyusingdeeplearningalgorithms
AT ahamedtofael sootymolddetectiononcitrustreecanopyusingdeeplearningalgorithms