Design, fabrication and evaluation of a robot for plant nutrient monitoring in greenhouse (case study: Iron nutrient in spinach)

Abstract

The regulation of nutrients intake in plant life is a critical factor in preserving the quality of crops, enhancing yield rates, and minimizing the fertilizer consumption. Consequently, this research presents the design of an intelligent robot for real-time monitoring of plant nutrients within greenhouse environments. The robot’s ability to detect and address iron deficiency in spinach plants was evaluated. To achieve this, the robot, equipped with a camera, captured visible imaging from surface of spinach plants including both the control group and the group subjected to iron deficiency stress. Statistical analysis of the data was performed at the probability level of 5 %, and genetic algorithm (GA) was utilized to select the most suitable features (G, Area, Energy, Entropy, R and L), from 41 extracted image parameters, including color, morphological, and texture features. An artificial neural network (ANN) was then employed to detect iron deficiency in plants, resulting in an accuracy of 96 %. Subsequently, the Gaussian kernel function of support vector regression (SVR) algorithm was used to predict spinach iron content, yielding the highest determination coefficient (R2 = 0.94) and the lowest values of mean absolute percentage error (MAPE = 5.92), root mean square error (RMSE = 0.43) and standard regression error (SRE = 2.66). The designed robot detected iron-deficient plants with precision, sensitivity, specificity and accuracy of 86 %, 82 %, 84 % and 83 %, respectively. The robot then took the necessary control actions to supply the deficiency of this nutrient by foliar spraying of more than 89 % of the crop area.