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- W4362680546 abstract "The conceptual and technological reasons, with the fast and reliable geographical categorization of crop kinds, can rely on remote sensing data is a critical task. Insurance policy, land leasing, supply-chain logistics, and financial market forecasts utilize spatial variability crop-type information to predict crop acreage for a range of monitoring and decision-making purposes. Cropland products derived from remote sensing have become much more accurate in depicting the location and size of agricultural fields. However, differentiating between actively agricultural areas and probably followed fields across agricultural lands, particularly in temperate zone grass barren structures, has received little attention. Crop-fallow rotation techniques are extensively utilized for soil fertility regeneration in the Sahel, one of the world's biggest dryland areas. Nevertheless, since fallow fields aren't clearly distinguished even within cropland class in any current remote sensing-based land use/cover maps, irrespective of the geographical resolution, nothing is recognized about their size. As one of the most precise metrics for evaluating regression modelling, the recently created Random Forest (RF) machine-learning method has gained widespread recognition. One study sought to determine whether the RF regression algorithm could be used to estimate crop solar energy digitally, assess a system's implementation, and compare the RF algorithm's productivity to the existing benchmarks. Results show that the RF-based technique is far better and outperforms the existing benchmarks in terms of performance." @default.
- W4362680546 created "2023-04-08" @default.
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- W4362680546 date "2022-12-23" @default.
- W4362680546 modified "2023-10-14" @default.
- W4362680546 title "An advanced Machine Learning Techniques on Agriculture fileds for future level cultivations" @default.
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- W4362680546 doi "https://doi.org/10.1109/smartgencon56628.2022.10083658" @default.
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