European Academic Research ISSN 2286-4822
ISSN-L 2286-4822
Impact Factor: 3.4546 (UIF)
DRJI Value : 5.9 (B+)
Article Details :
Article Name :
Comparison between machine vision and manual leaf area estimation approaches for the precision horticulture management
Author Name :
Tabinda Naz Syed, Farman Ali Chandio, Sher Ali Sheikh, Raheem Bux Vistro, Imran Ali Lakhiar
Publisher :
Bridge Center
Article URL :
Abstract :
Easy, accurate, and economical method for determining the individual leaf area of the plants is a useful tool in agronomic and physiological studies. To increase agricultural yield and production with minimum cost, farmers are adopting different modern and precision farming techniques in traditional agriculture. In this study, three different leaf area measuring methods (A1= image processing method, A2= leaf area meter, and A3= graphical method) were compared by selecting sixty leaves (with different shapes and sizes) of six different plant species. In addition, A1 and A2 were performed by using Photoshop CS6 software and a laser leaf area meter (CI-203) respectively, and simple graph paper was used for A3. The experimental results indicated that the value of adjusted R^2 0.9988 was obtained with a relationship between the A1 and A2 methods. However, the value of adjusted R^2 0.9992 was obtained for the relationship between A1 and A3 methods. Therefore, this study concluded that the A1 method obtained sufficient accuracy in contrast to A2 and A3 methods for the leaf area measurement. The important characteristics of the A1 method are its rapidness, easiness, and suitability for precise and cost-efficient estimates. Thus, the A1 method can be used to measure the leaf area of any plant in many different experiments without using expensive equipments.
Keywords :
Non-destructive; Image processing; Leaf area; Digital images
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