Identification of sugarcane maturity scale based on RGB, Gabor feature extraction and Support Vector Machine

3rd Annual Applied Science and Engineering Conference

工业技术;自然科学

Rahmad, C.^1 ; Rahutomo, F.^1 ; Gustalika, M.A.^2 ; Rahmah, I.F.^2

Information Technology Department, State Polytechnic of Malang, Malang, Indonesia^1

Electrical Engineering Department, State Polytechnic of Malang, Malang, Indonesia^2

关键词: Artificial neural network methods;    Classification process;    Gabor feature extraction;    Gabor texture features;    New approaches;    Plant growth;    Precision and recall;    Three categories;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012065/pdf
DOI  :  10.1088/1757-899X/434/1/012065

来源: IOP

【 摘 要 】

Sugarcane is a unique grass variety that allows many cuts for more than a few years. Plant growth is very slow in the early stages that takes 25-30 days to complete germination and 90-95 more days to complete the tillers. Small scale sugarcane farmers generally do not have direct access to sugar mills but through loggers. Sugarcane farmer must increase cost for the loggers by themself. Sugarcane farmers often forgot the time at the moment when they planted sugarcane. This study proposes a new approach to establish detection of maturity of sugarcane before harvesting by detecting through stem of sugarcane. This study illustrates the maturity of sugarcane. This study used its own datasets of 300 stem images of sugarcane and to differentiate into three categories, immature, semi-mature, and mature. The study is divided into two processes, the first one is extraction using the RGB color feature and Gabor texture feature, and the second is the classification process using the Support Vector Machine, Naive Bayes and Artificial Neural Network methods. Based on the trial it is known that Support Vector Machine has the best results with precision and recall of 87,4% and 85,7% respectively.