Detecting and Counting Pistachio Psylla Pest Using Machine Vision in Laboratory Condition

Subject Areas : Sustainable production technologies

Mohammad Ghorbani ¹ , Mohammadmehdi Maharlooei ² , Kamal Ahmadi ³

1 - Graduate MSc student; Biosystems Engineering Dept; School of Agriculture; Shahid Bahonar University of Kerman; Kerman; Iran.
2 - Associate Professor; Biosystems Engineering Dept; School of Agriculture; Shahid Bahonar University of Kerman; Kerman; Iran.
3 - Assistant Professor; Plant Pathology Dept.; School of Agriculture; Shahid Bahonar University of Kerman; Kerman; Iran.

Received: 2022-01-25 Accepted : 2022-01-31 Published : 2022-02-20

Keywords: image processing, Lighting condition, detection, Psylla pest, Count,

Abstract :

Plant diseases and pest damages are one of the main factors that reduce both quality and quantity of final crops and restrict growers profit. Problems in photosynthesis and evapotranspiration may be taken place due to these pathogens. Efforts to apply chemicals or employing other methods for pest and disease control need precise field scouting and experts to identify the problem in a timely manner. Psylla pest is one of the most prevalent pests in pistachio orchards, which causes irreparable damage to orchards every year. In this study, the feasibility of employing machine vision to discriminate and count Pistachio Psylla was evaluated. Field data were collected from research orchards in three different time slots in summer based on pest infestation. The images were taken by various cellphone cameras with different resolutions in high and low controlled lighting conditions. The results of image-based count were compared with manual count by the expert technician in the laboratory. The effect of different light conditions and cameras with different resolutions on pest detection were evaluated by ANOVA test. There was no significant difference between manual count and digital count in high lighting conditions, but the differences in low lighting conditions were significant (p<0.05). The incorrect classification percentage values for low lighting conditions were higher than the ones obtained for high lighting conditions. This could be due to the lower quality of the images, in higher ISO values in low lighting conditions.The results showed that images taken by low-cost cameras in proper light intensity can easily replace the time-consuming and labor-intensive method of manual count.