Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration

Abstract

Leaf wetness duration (LWD) is a critical parameter used to predict plant disease, but its determination under actual field conditions is a major challenge. In this study, a method for determining LWD using thermal infrared imaging was developed and applied to cucumber plants grown in a solar greenhouse. Thermal images of the plant leaves were captured using an infrared scanning camera, and a leaf wetness area segmentation method consisting of two procedures was applied. First, a color space conversion was performed automatically by an image-processing algorithm. Then, the K-means clustering algorithm was applied to enable the segmentation of the wetness area on the thermal image. Subsequently, to enable overall thermal image analysis, an initial leaf wetness threshold (LWT) of 5% was defined (where wetness values higher than 5% indicated that the leaf was in a wet state). The results of comparative experiments conducted using thermal images of plant leaves captured using an infrared scanning camera and human visual observation indicated that the estimated LWD values were generally higher than the observed LWD values, because slight leaf wetness condensations were overlooked by the human eye but detected by the infrared scanning camera. While these differences were not found to be statistically significant in this study, the proposed method for determining LWD using thermal infrared imaging may provide a new LWD detection method for cucumber and other plants grown in solar greenhouses. Keywords: thermal imaging, K-means clustering algorithm, leaf wetness duration, cucumber DOI: 10.25165/j.ijabe.20201303.4301 Citation: Wen D M, Ren A X, Ji T, Flores-Parra I M, Yang X T, Li M. Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration. Int J Agric & Biol Eng, 2020; 13(3): 161–167.