Design of Intelligent Acquisition System for Tomato Leaf Area

Leaf area of plants as an important parameter model of plant growth can directly reflect the growth conditions of plants, it can be monitored growth management science effective guidance of the plant. Traditional plant leaf area detection means complex operation, over-reliance on human, high price, low precision, multi-contact measurement, easily damaged leaf blade. Based on this, we constructed a tomato leaf area online monitoring system for non-destructive, self-designed hardware platform, developed its system software and conducted comparative tests to measure leaf area based on the system. This paper test the model and the traditional model, the error values of the total area and projected area of the model are 1.02% and 0.64%, respectively, the error rate of vertical projection and total area ratio value is 0.42%.