Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems

Abstract

Chlorosis, or leaf yellowing, in crops is one of the quality issues that primarily occurs due to interference in the production of chlorophyll contents. The primary contributors to inadequate chlorophyll levels are abiotic stresses, such as inadequate environmental conditions (temperature, illumination, humidity, etc.), improper nutrient supply, and poor water quality. Various techniques have been developed over the years to identify leaf chlorosis and assess the quality of crops, including visual inspection, chemical analyses, and hyperspectral imaging. However, these techniques are expensive, time-consuming, or require special skills and precise equipment. Recently, computer vision techniques have been implemented in the agriculture field to determine the quality of crops. Computer vision models are accurate, fast, and non-destructive, but they require a lot of data to achieve high performance. In this study, an image processing-based solution is proposed to solve these problems and provide an easier, cheaper, and faster approach for identifying the chlorosis in lettuce crops grown in an aquaponics facility based on their sensory property, foliage color. The ‘HSV space segmentation’ technique is used to segment the lettuce crop images and extract red (R), green (G), and blue (B) channel values. The mean values of the RGB channels are computed, and a color distance model is used to determine the distance between the computed values and threshold values. A binary indicator is defined, which serves as the crop quality indicator associated with foliage color. The model’s performance is evaluated, achieving an accuracy of 95%. The final model is integrated with the ontology model through a cloud-based application that contains knowledge related to abiotic stresses and causes responsible for lettuce foliage chlorosis. This knowledge can be automatically extracted and used to take precautionary measures in a timely manner. The proposed application finds its significance as a decision support system that can automate crop quality monitoring in an aquaponics farm and assist agricultural practitioners in decision-making processes regarding crop stress management.