Applying Normalized Difference Vegetation Index from UAV for Fertilizer Cost Reduction in Rice RD33 Cultivation
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Abstract
Due to the advanced technology in this era, farmers and government agencies are increasingly using UAVs to assist in cultivation and agriculture management, especially to reduce labor costs while increase agricultural productivity. Thus, we use of remote sensing data obtained from a drone and supervised classification for rice cultivation management to be more efficient. The specific objectives include 1) to analyze the growth of rice RD33 using the Normalized Difference Vegetation Index (NDVI) from an UAV and 2) to classify the cultivated area of rice RD33 from the NDVI image for analyzing the cost of fertilizer. Hua Taphan Model in Amnat Charoen Province is used as the study area in this study. Regarding the results, NDVI of rice RD33 showed the highest values during the reproductive phase (NDVI values between 0.2-0.4), and gradually decreases during the flowering and maturity phase. For data classification, the experimental farm was categorized into 3 classes: bare soil, low-density rice growing and high-density rice growing. Confusion matrix was used for assessing the overall accuracy and the Kappa coefficient which are 80 percent and 0.68 respectively. This research has applied the classification technique to determine the appropriate amount of fertilizer. The results of the research can reduce the amount of fertilizer during rice ripening phase by 6.67 percent.
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