Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture

Abbas, Aqleem; Zhang, Zhenhao; Zheng, Hongxia; Alami, Mohammad Murtaza; Alrefaei, Abdulmajeed F.; Abbas, Qamar; Naqvi, Syed Atif Hasan; Rao, Muhammad Junaid; Mosa, Walid F. A.; Abbas, Qamar; Hussain, Azhar; Hassan, Muhammad Zeeshan; Zhou, Lei

doi:10.3390/agronomy13061524

Open AccessReview

Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture

by

Aqleem Abbas

^1,2,†

,

Zhenhao Zhang

^1,†,

Hongxia Zheng

¹,

Mohammad Murtaza Alami

³

,

Abdulmajeed F. Alrefaei

⁴

,

Qamar Abbas

⁵

,

Syed Atif Hasan Naqvi

^6,*

,

Muhammad Junaid Rao

⁷

,

Walid F. A. Mosa

⁸

,

Qamar Abbas

⁹,

Azhar Hussain

²,

Muhammad Zeeshan Hassan

⁶ and

Lei Zhou

^1,*

¹

State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China

²

Department of Agriculture and Food Technology, Karakoram International University, Gilgit 15100, Pakistan

³

Department of Crop Cultivation and Farming System, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

⁴

Department of Biology, Jamoum University Collage, Umm Al-Qura University, Makkah 21955, Saudi Arabia

⁵

Department of Computer Sciences, University of Karachi, Karachi 75270, Pakistan

⁶

Department of Plant Pathology, Bahauddin Zakariya University, Multan 60800, Pakistan

⁷

State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Key Laboratory of Sugarcane Biology, College of Agriculture, Guangxi University, Nanning 530004, China

⁸

Plant Production Department (Horticulture-Pomology), Faculty of Agriculture, Saba Basha, Alexandria University, Alexandria 21531, Egypt

⁹

Department of Plant Sciences, Karakoram International University, Gilgit 15100, Pakistan

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Agronomy 2023, 13(6), 1524; https://doi.org/10.3390/agronomy13061524

Submission received: 5 April 2023 / Revised: 23 May 2023 / Accepted: 26 May 2023 / Published: 31 May 2023

(This article belongs to the Special Issue Remote Sensing in Smart Agriculture)

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Abstract

Plant diseases are one of the major threats to global food production. Efficient monitoring and detection of plant pathogens are instrumental in restricting and effectively managing the spread of the disease and reducing the cost of pesticides. Traditional, molecular, and serological methods that are widely used for plant disease detection are often ineffective if not applied during the initial stages of pathogenesis, when no or very weak symptoms appear. Moreover, they are almost useless in acquiring spatialized diagnostic results on plant diseases. On the other hand, remote sensing (RS) techniques utilizing drones are very effective for the rapid identification of plant diseases in their early stages. Currently, drones, play a pivotal role in the monitoring of plant pathogen spread, detection, and diagnosis to ensure crops’ health status. The advantages of drone technology include high spatial resolution (as several sensors are carried aboard), high efficiency, usage flexibility, and more significantly, quick detection of plant diseases across a large area with low cost, reliability, and provision of high-resolution data. Drone technology employs an automated procedure that begins with gathering images of diseased plants using various sensors and cameras. After extracting features, image processing approaches use the appropriate traditional machine learning or deep learning algorithms. Features are extracted from images of leaves using edge detection and histogram equalization methods. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Drones may be able to provide early warning of plant diseases, allowing farmers to prevent costly crop failures.

Keywords: plant disease detection; drones; machine learning; precision agriculture; image analysis

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MDPI and ACS Style

Abbas, A.; Zhang, Z.; Zheng, H.; Alami, M.M.; Alrefaei, A.F.; Abbas, Q.; Naqvi, S.A.H.; Rao, M.J.; Mosa, W.F.A.; Abbas, Q.; et al. Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture. Agronomy 2023, 13, 1524. https://doi.org/10.3390/agronomy13061524

AMA Style

Abbas A, Zhang Z, Zheng H, Alami MM, Alrefaei AF, Abbas Q, Naqvi SAH, Rao MJ, Mosa WFA, Abbas Q, et al. Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture. Agronomy. 2023; 13(6):1524. https://doi.org/10.3390/agronomy13061524

Chicago/Turabian Style

Abbas, Aqleem, Zhenhao Zhang, Hongxia Zheng, Mohammad Murtaza Alami, Abdulmajeed F. Alrefaei, Qamar Abbas, Syed Atif Hasan Naqvi, Muhammad Junaid Rao, Walid F. A. Mosa, Qamar Abbas, and et al. 2023. "Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture" Agronomy 13, no. 6: 1524. https://doi.org/10.3390/agronomy13061524

APA Style

Abbas, A., Zhang, Z., Zheng, H., Alami, M. M., Alrefaei, A. F., Abbas, Q., Naqvi, S. A. H., Rao, M. J., Mosa, W. F. A., Abbas, Q., Hussain, A., Hassan, M. Z., & Zhou, L. (2023). Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture. Agronomy, 13(6), 1524. https://doi.org/10.3390/agronomy13061524

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Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture

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