首页|Recent Findings in Machine Learning Described by a Researcher from Prince of Son gkla University (Oil Palm Bunch Ripeness Classification and Plantation Verificat ion Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization )
Recent Findings in Machine Learning Described by a Researcher from Prince of Son gkla University (Oil Palm Bunch Ripeness Classification and Plantation Verificat ion Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Surat T hani, Thailand, by NewsRx editors, the research stated, "Oil palm cultivation th rives as a prominent agricultural endeavor within the southern region of Thailan d, where the country ranks third globally in production, following Malaysia and Indonesia." Financial supporters for this research include National Science, Research And In novation Fund (Nsrf) And Prince of Songkla Universit. Our news correspondents obtained a quote from the research from Prince of Songkl a University: "The assessment of oil palm bunch ripeness serves various purposes , notably in determining purchasing prices, pre-harvest evaluations, and evaluat ing the impacts of disasters or low market prices. Presently, two predominant me thods are employed for this assessment, namely human evaluation, and machine lea rning for ripeness classification. Human assessment, while boasting high accurac y, necessitates the involvement of farmers or experts, resulting in prolonged pr ocessing times, especially when dealing with extensive datasets or dispersed fie lds. Conversely, machine learning, although capable of accurately classifying ha rvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ri peness classification. Considering these challenges, this study introduces the d evelopment of a classification platform leveraging machine learning (deep learni ng) in conjunction with geospatial analysis and visualization to ascertain the r ipeness of oil palm bunches while they are still on the tree. The research outco mes demonstrate that oil palm bunch ripeness can be accurately and efficiently c lassified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.1 2% with 1160 images."
Prince of Songkla UniversitySurat Than iThailandAsiaCyborgsEmerging TechnologiesMachine Learning
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