Data Synthesis for Alfalfa Biomass Yield Estimation
نویسندگان
چکیده
Alfalfa is critical to global food security, and its data abundant in the U.S. nationally, but often scarce locally, limiting potential performance of machine learning (ML) models predicting alfalfa biomass yields. Training ML on local-only results very low estimation accuracy when datasets are small. Therefore, we explore synthesizing non-local estimate yields labeled as high, medium, or low. One option remedy local train using data; however, this only works about well data. propose a novel pipeline that trains synthesized from crop Our pipeline, training (SNLT pronounced like sunlight), achieves gain 42.9% over best regular our small target dataset. This produced highest 85.7% with decision tree classifier. From these results, conclude SNLT can be useful tool helping ML. Furthermore, software application called Predict Your CropS (PYCS Pisces) designed help farmers researchers predict based pretrained models.
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ژورنال
عنوان ژورنال: AI
سال: 2022
ISSN: ['2673-2688']
DOI: https://doi.org/10.3390/ai4010001