Design and implementation of a smart system for water management of lilium flower using image processing

Abstract

Agriculture is one of the largest and most important water consuming sector in the world. More than 80% of water resources are wasted in this sector because of the lack of advanced technology. Therefore, by saving water and controlling the growth conditions of the plant simultaneously, a low level of water consumption can be achieved. In this research, an automated machine vision system was designed and developed to determine the water requirement of the Lilium plant. Therefore, the average values of each RGB, Lab and HSV channel of leaves and stem along with curvature, length and orientation of stem were extracted. After that, features were analyzed using Duncan's multiple range test and the linguistic hedges feature selection algorithm combined with adaptive neuro-fuzzy classifier. According to the results, Hs, curvature, stem angle, HL, BL and LL were the most significant features and the classifier accuracy for four plant conditions (100% of field capacity (FC), 80% of FC, 60% of FC and 40% of FC) were 84.84, 84.21, 86.95 and 80%, respectively. The results showed that the proposed system has the ability to determine the levels of water stress and provide the amount of water requirement.