Journal of Researches in Mechanics of Agricultural Machinery

Journal of Researches in Mechanics of Agricultural Machinery

Identification of sunn pest (Eurygaster) in cereal farms using UAV image processing technique

Document Type : Research Paper

Authors
somaye Shadmanfar 1
amin lotfalian dehkordi 2
ahmad reza Ghasemi Dastgerdi 3
Alireza Haghshenas Nasrabadi 4
1 M.Sc., Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.
2 Assistant Professor, Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.
3 Associate Professor, Water Engineering Department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.
4 Lecturer, Plant Protection Research Department, Isfahan Agricultural & Natural Resources Research Center, Isfahan, Iran
10.22034/jrmam.2023.13941.596
Abstract
Considering that wheat and barley are important and strategic products in Iran, the Sunn pest is one of the most important pests of these products. Every year, the country's plant protection organization monitors this dangerous pest so that an order to fight it can be issued at the right time. Farm monitoring is a very difficult, time-consuming, and destructive task; it also requires a large number of specialized human resources. One of the goals of this research is to create a non-destructive method for such an operation in the identification of Sunn pests. In this research, the counting of Sunn pests in cereal farms was done by two methods using aerial image processing by recording 954 images and framing method and eye counting in the farm by an expert in two farms with different numbers of this pest. The number of pests in each image was obtained after processing the images using the Python programming language in the PyCharm learning library. The statistical study results showed no significant difference at the 1% probability level between the average data of the two methods. This method can be used confidently to monitor the Sunn pest in cereal farms. In this research, by using UAV image processing, a fast, accurate, and non-destructive method was proposed and evaluated for counting the pests and issuing spraying orders in cereal farms.
Keywords
Image processing
UAV
Sunn pest
Eurygaster counting
Machine learning
Subjects
Abasi. K., Almasi. M., Barghae. A. M., & Minae. S. (2014). Performance model of basic products based on the level of agricultural mechanization in Iran. Journal of Agricultural Machinery, 4: 351-344. (In Persian)
 
Ahmadi. K., Ebadzade. H., Hatami. F., Adbshah. H., & Kazemiyan. A., (2019). Agricultural statistics of the crop year 1396-97. Volume One: 18. (In Persian)
 
Allahyari, M. Bandani, A. R. & Habibi-Rezaei, M. (2010). Subcellular fractionation of midgut cells of the sunn pest Eurygaster integriceps (Hemiptera: Scutelleridae): Enzyme markers of microvillar and perimicrovillar membranes. Journal of Insect Physiology, 56: 710-717.
 
Anonymous (2020) Instructions of the National Plant Protection Organization of Iran, Technical and executive instructions to combat grain age in the 99-98 crop year. (In Persian)
 
Bagheri, N. & Moazzen, S. A. (2009). Optimum Strategy for Agricultural Mechanization Development in Iran, Journal Agricultural of Technology, 6(1): 225-237.
 
Chanyoung, Ju. & Hyoung, Son. (2018). Multiple UAV Systems for Agricultural Applications:Control, Implementation, and Evaluation. Electronics, 162 :1-19.
 
Duric, V., Mlandenov, N., Hristov, N., Kondic-Spika, A., Acin, V. & Racic, M. (2014). The Effect of Sunn Pest Infested Grains on Wheat Quality in Different Field Conditions. Romanian Agricultural Research, 31: 323-330.
 
Ebadzadeh. H., Ahmadi. K., Mohamadinia Afrozi. Sh., Abastaghani. R, Abasi. M., & Yari. Sh., (2018). Agricultural Statistics of 1396. Volume II: 68. (In Persian)
 
F.A.O. (2009). Sunn Pests and their Control in the Near East, F.A.O Corporate Document Respository, pp. 1-17.
 
Heydari. A., & Amiri Pariyan. J., (2020) Identification and enumeration of two important greenhouse pests with the help of image processing: case study of whitefly and thrips. Journal of Agricultural Machinery, 9: 320-309. (In Persian)
 
Hosseini, S. A., Masoudi, H., Sajjadieh, S. M., & Abdanan-Mehdizadeh, S. (2019) Determining the amount of nitrogen and chlorophyll of sugarcane plant from the color indices of digital aerial images with regression modeling. Agricultural Engineering Scientific Journal of Agriculture. Volume 42: 98-83. (In Persian)
 
Johnson, J. B. (2020). An overview of near-infrared spectroscopy (NIRS) for the detection of insect pests in stored grains. Journal of Stored Products Research, 86: 101558. https://doi.org/10.1016/J.JSPR.2019.101558
 
Kasinathan, T., Singaraju, D., & Uyyala, S. R. (2021). Insect classification and detection in field crops using modern machine learning techniques. Information Processing in Agriculture, 8(3): 446-457.
 
Kavoosi, Z. & Raoufat, M. H. 2020. Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System. Journal of Agricultural Machinery, 10: 23-35.
 
Khanjani, M. (2008). Field Crop Pest in Iran, Bu-Ali Sina University Pub, 195: 108.
 
Moonrinta, J., Chaivivatrakul, S., Dailey, MN. & Ekpanyapong, M. (2010). Fruit detection, tracking, and 3D reconstruction for crop mapping and yield estimation. Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on: 1181-1186.
 
Rajabi, Gh. (2007). Sunn Pest Management Based on its Outbreaks' Key Factor Analysis in Iran. Agricultural Education Publications, Tehran, Iran. 163p.
 
Rajabi, GH.R. & Behvarzin, M. (2003). The Sunn Pests and Farm Land Diseases in Iran,the Institute of Researches and Education, the Agricultural Education Issue, 4-6.
 
Rezabaygi, M. & Rajabi, GH.R. (2006). The Important Sunn Pests of Wheat and it’s Control Management in Iran.