# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 1 Introduction  
Aquaponics is a controlled environment agriculture practice that combines aquaculture (farming of fish), hydroponics (soilless growing of plants), and nitriifying bacteria in a symbiotic environment. This agricultural technique promises to be a suitable alternative to global environmental and food problems [1, 2]. Little gem romaine lettuce is one of the most common crops grown in the aquaponics system because it has a high growth rate, short growth cycle, high planting density, and low energy demand [3]. Just like traditional agriculture, lettuce crops grown in aquaponics may face abiotic stresses, such as inadequate environmental conditions (humidity, temperature, illumination, etc. ), irregular supply of nutrient-enriched water due to the inaccurate design of the system, poor water quality (improper pH), and insufficient concentrations of required minerals, such as N-NO\({}_{3}\), P, K,Ca, and Mg in the effluent [4; 5]. These stresses adversely impact the growth and quality of lettuce in a plant factory. In addition to yields, the quality of crops is essential for market acceptance as they affect consumers' purchase behavior [6]. Hence, it is vital to maintain the quality of crops and rectify the factors impacting them. The quality of crops is assessed using morphological traits (crop height, width, area, and volume), biomass production, nutritional value, and sensory attributes (color, texture, smell, and taste) [7]. Visual indices, such as size, appearance, and green color, are the obvious quality indicators of lettuce that greatly impact consumers' buying attitudes [6]. In this essence, these indices can be used to determine the quality of lettuce crops in a plant factory. Particularly, foliage color, which determines the chlorophyll content, is one of the key quality indicators [8]. The green color of the foliage represents that the crop is healthy, and the yellow color signifies that the crop is suffering from chlorosis. Leaf chlorosis is generally caused by different types of stresses, such as irregular illumination or temperature conditions, etc., which cause interference in the production of chlorophyll contents [4]. The irregular chlorophyll content levels represent the deficiency of secondary metabolites in lettuce, such as phenolic compounds, vitamins A and C, and carotenoid, which enhances the anti-oxidation ability of the human body and the suppression of inflammatory disease and cancer [9]. In order to achieve high-quality crops, it is necessary to identify leaf chlorosis and abiotic stresses by monitoring the crop throughout the growth cycle. The conventional method to identify leaf chlorosis and plant quality is based on visual observation, requiring certain expertise from agriculture practitioners [10]. Visual detection, however, is a time-consuming and laborious task, and there is a probability of misdiagnosis, especially in the early growth stages [10]. Other methods include chemical analyses and leaf color chart (LCC) matching, which, again, are costly, time-consuming, and destructive techniques. Chemical methods involve the collection of plant tissue for laboratory analyses of plant leaves. The Kjeldahl digestion assay is one of the most widely used chemical methods [11]. Although this method is accurate, sample preprocessing and delays in laboratory analyses hinder its widespread usage. The standard LCC tool is also available and used as a reference to estimate leaf color and plant quality [12]. This technique is widely used in many countries but is a manual inspection process and, hence, time-consuming. In order to overcome these challenges, agriculture methods have been automated for years, and, hence, several non-destructive methods have been proposed to detect leaf chlorosis and plant quality. One of the methods is the spectral reflection method, which uses the property of chlorophyll with different reflection intensities at different wavebands to assess the quality of the plant.