文献类型： 外文期刊

作者： Abbas, Aqleem ¹ ; Zhang, Zhenhao ¹ ; Zheng, Hongxia ¹ ; Alami, Mohammad Murtaza ³ ; Alrefaei, Abdulmajeed F. ⁴ ; Abbas, Qamar ⁵ ; Naqvi, Syed Atif Hasan ⁶ ; Rao, Muhammad Junaid ⁷ ; Mosa, Walid F. A. ⁸ ; Abbas, Qamar ⁵ ; Hussin, Azhar ² ; Hassan, Muhammad Zeeshan ⁶ ; Zhou, Lei ¹ ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China

2.Karakoram Int Univ, Dept Agr & Food Technol, Gilgit 15100, Pakistan

3.Huazhong Agr Univ, Coll Plant Sci & Technol, Dept Crop Cultivat & Farming Syst, Wuhan 430070, Peoples R China

4.Umm Al Qura Univ, Jamoum Univ Collage, Dept Biol, Mecca 21955, Saudi Arabia

5.Univ Karachi, Dept Comp Sci, Karachi 75270, Pakistan

6.Bahauddin Zakariya Univ, Dept Plant Pathol, Multan 60800, Pakistan

7.Guangxi Univ, Coll Agr, State Key Lab Conservat & Utilizat Subtrop Agrobio, Guangxi Key Lab Sugarcane Biol, Nanning 530004, Peoples R China

8.Alexandria Univ, Fac Agr, Plant Prod Dept Hort Pomol, Alexandria 21531, Egypt

9.Karakoram Int Univ, Dept Plant Sci, Gilgit 15100, Pakistan

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

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 6 期

页码：

收录情况： SCI

摘要： 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.