Integration of smartphone technology for maize recognition. Issue 1 (November 2021)

• Record Type: Journal Article
• Authors: Aqil, M.; Tabri, F.; Andayani, N. N.; Panikkai, S.; Suwardi, ; Efendi, Roy; Bunyamin, Z; Azrai, M.; Ratule, Taufiq
• Is Part Of: IOP conference series. Volume 911:Issue 1(2021)
• Publication Date: 2021-11
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/911/1/012037
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: The study of android based maize assessment was done by involving two popular machine learning software i.e. teachable machine and android studio. The classification model was performed in online teachable machine learning while interface generation was performed in android studio. Various maize tassel from male, female and contamination plants were collected and used for training and model validation. The results indicated that Android-based tassel classification was successfully applied to the study area with accuracy of 80.7%. In addition, the error of classification was 19.3%, a relatively lower values for large testing datasets. Several mis-classification were found particularly at similar tassel shape. The integration of the model with smartphone technology enables rapid recognition of off-type plant at real-time, even though operated by personnel with limited skills or no knowledge seed technology on maize parental lines ideotype.