Quality assessment of row crop plants by using a machine vision system

Abstract

This paper reports research results on developing a machine vision system to assess the quality of row crop plants. Comparing to the prevalent machine vision system employed in agricultural industry for weed-crops classification as well as plant density evaluation, the proposed machine vision system is able to detect the location of plants (weed / crops) and calculate the leaves' area for plant quality assessment, even if the leaves are overlapped with each other. The developed machine vision system involves a camera system and an image processing system. The camera system uses a coaxial camera constructed by a RGB sensor and near infrared (NIR) sensor, which cooperate with a white front lighting and NIR front lighting respectively. Plants are firstly captured by the coaxial camera. The plants are segmented from background on RGB image; the overlapping edges of leaves are detected on NIR image. Afterwards the overlapping leaves are separated and assigned to the assessed stem position of plants. At last, based on the assigned leaves, the plants are separated, and the area of plant canopy is calculated. A set of experiments have been made to prove the feasibility of the proposed machine vision system.