On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Houda Orchi, Mohamed Sadik and Mohammed Khaldoun
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Houda Orchi: Networking Embedded Systems and Telecommunications (NEST) Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 8118, Morocco
Mohamed Sadik: Networking Embedded Systems and Telecommunications (NEST) Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 8118, Morocco
Mohammed Khaldoun: Networking Embedded Systems and Telecommunications (NEST) Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 8118, Morocco

Agriculture, 2021, vol. 12, issue 1, 1-29

Abstract: The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

Keywords: machine learning; deep learning; image processing; hyperspectral image analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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