The effects of variety and tapping length on several physical, biochemical, nutritional and micro-biological parameters (viscosity, pH, Total Titratable Acidity (TTA), fermenting microorganisms, contaminants) of palm ...The effects of variety and tapping length on several physical, biochemical, nutritional and micro-biological parameters (viscosity, pH, Total Titratable Acidity (TTA), fermenting microorganisms, contaminants) of palm wine extracted from two varieties of palm oil tree (Dura and Tenera) were studied. Each variety presented its own palm wine specific characteristics regarding the chosen parameters. Microbiological and biochemical contents of palm wine were determined during the tapping of Dura and Tenera felled oil palm trees for 4 weeks. Some differences in chemical compositions between fresh palm wine samples of two palm trees varieties were observed. The exudates obtained during the first day of tapping of Dura palm wine were very sugary, less sour and did not contain substantial concentrations of alcohol but the highest loads of microorganisms were observed in Tenera palm wine. Throughout the tapping of palm wine, yeasts and lactic acid bacteria population changed with undoubtedly influence on the palm wine quality.展开更多
Oil palm tree monitoring is essential to track productivity and prevent diseases,but manual methods are labor-intensive,cost-prohibitive,and error-prone.While existing remote sensing data and deep learning methods off...Oil palm tree monitoring is essential to track productivity and prevent diseases,but manual methods are labor-intensive,cost-prohibitive,and error-prone.While existing remote sensing data and deep learning methods offer effi cient alternatives for monitor-ing,achieving a highly accurate tree condition classification remains a challenge.Therefore,this study classifies healthy,yellow,small,and dead oil palm trees using the YOLOv8 model.A publicly available multi-class oil palm tree dataset,after extensive correction of identified labeling errors,is used to train the model.The model's performance is compared with state-of-the-art object detectors,and a prototype web application is developed to test the model on adverse unseen image scenes.The YOLOv8 model achieves up to 99.7%F1-score and 99.3%mAP across all classes of the corrected dataset.It outperforms other object detectors and produces similar scores as the recent YOLOv10-large model.Further validation on unseen images from the developed prototype results in 76.0%F1-score and 77.9%mAP across all classes.Finally,implications empha-size the role of YoLOv8 in handling class imbalance resulting from underrepresented classes in the dataset.The experimental findings,practical demonstration,and implications presented in this paper offer robust and reliable monitoring of oil palm trees with innova-tions in precision agriculture.展开更多
文摘The effects of variety and tapping length on several physical, biochemical, nutritional and micro-biological parameters (viscosity, pH, Total Titratable Acidity (TTA), fermenting microorganisms, contaminants) of palm wine extracted from two varieties of palm oil tree (Dura and Tenera) were studied. Each variety presented its own palm wine specific characteristics regarding the chosen parameters. Microbiological and biochemical contents of palm wine were determined during the tapping of Dura and Tenera felled oil palm trees for 4 weeks. Some differences in chemical compositions between fresh palm wine samples of two palm trees varieties were observed. The exudates obtained during the first day of tapping of Dura palm wine were very sugary, less sour and did not contain substantial concentrations of alcohol but the highest loads of microorganisms were observed in Tenera palm wine. Throughout the tapping of palm wine, yeasts and lactic acid bacteria population changed with undoubtedly influence on the palm wine quality.
基金funded by Thammasat University,Contract No.12/2565(TUFT 12/2565).
文摘Oil palm tree monitoring is essential to track productivity and prevent diseases,but manual methods are labor-intensive,cost-prohibitive,and error-prone.While existing remote sensing data and deep learning methods offer effi cient alternatives for monitor-ing,achieving a highly accurate tree condition classification remains a challenge.Therefore,this study classifies healthy,yellow,small,and dead oil palm trees using the YOLOv8 model.A publicly available multi-class oil palm tree dataset,after extensive correction of identified labeling errors,is used to train the model.The model's performance is compared with state-of-the-art object detectors,and a prototype web application is developed to test the model on adverse unseen image scenes.The YOLOv8 model achieves up to 99.7%F1-score and 99.3%mAP across all classes of the corrected dataset.It outperforms other object detectors and produces similar scores as the recent YOLOv10-large model.Further validation on unseen images from the developed prototype results in 76.0%F1-score and 77.9%mAP across all classes.Finally,implications empha-size the role of YoLOv8 in handling class imbalance resulting from underrepresented classes in the dataset.The experimental findings,practical demonstration,and implications presented in this paper offer robust and reliable monitoring of oil palm trees with innova-tions in precision agriculture.
