Classification of oil palm female inflorescences anthesis stages using machine learning approaches

作     者：Mamehgol Yousefi D.B A.S.Mohd Rafie Samsuzana Abd Aziz Syaril Azrad Mohamed Mazmira Mohd Masri Ahmad Shahi O.F.Marzuki 

作者机构：Department.of Aerospace EngineeringUniversiti Putra MalaysiaSerdangSelangor 43400Malaysia Smart Farming Technology Research CentreDept.of Biological and Agricultural EngineeringUniversiti Putra MalaysiaSerdangSelangor 43400Malaysia MPOB Biological Research DivisionPersiaran InstitusiKawasan Institusi Bangi43000 KajangSelangorMalaysia Auckland CBDAuckland 1010New Zealand Faculty of Agriculture and Food SciencesUnivesiti Putra Malaysia Bintulu CampusJalan Nyabau97008 Bintulu SarawakMalaysia 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2021年第8卷第4期

页      面：537-549页

核心收录：

学科分类：12[管理学] 0907[农学-林学] 0908[农学-水产] 08[工学] 09[农学] 0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0905[农学-畜牧学] 0707[理学-海洋科学] 081104[工学-模式识别与智能系统] 0906[农学-兽医学] 0829[工学-林业工程] 0901[农学-作物学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Universiti Putra Malaysia Malaysian Palm Oil Board, MPOB 

主　　题：Oil palm Pollination Anthesis stage Machine Learning Classification 

摘      要：Prediction of pollination stages in oil palm plantations is an important research area in precision *** palm is known as the most efficient commercial crop with the capacity to fulfill the growing global demand for vegetable oil ***,oil palm production dependence on pollination is experiencing issues with decreasing the actual ***,alternative methods in commercial plantations such as humanassisted pollination and recently Wireless Sensor Network(WSN)have been deployed despite their high economic costs due to labor *** palm assisted pollination requires precision,inspection,traceability,and validation processes in the ***,all these processes are performed by humans that can be associated with false assumptions,uncertainty,and pollination ***,Machine Learning(ML)approaches as a subset of the Artificial Intelligence(AI)domain provides efficient,cost-effective,and non-destructive solutions to determine these reproductive stages for future autonomous pollination *** goal was to reduce the variability of worker’s performance in oil palms,using ML algorithms to make expert decisions and reduce the risk related to a transient *** comparative empirical study examined and compared the performance of the Random Forest(RF)against k Nearest Neighbor(kNN)and Support Vector Machine(SVM)for classification of oil palm pre-anthesis and anthesis stages,dividing into four classes(1,2,3,and 4).These models were tested using thermal features(endogenous)individually and in combination with meteorological variables(exogenous).The performance of models is evaluated with specific measures of performance,such as overall user’s and producer’s accuracies and F-measure values derived from the confusion *** results showed that the RF model produced better results with regard to average Fmeasure(88.6%,71.83%),producer’s accuracies(88.70%,71.35%),and user’s accuracies(88.27%,72.36%)on test set