首页|Data on Machine Learning Reported by Bappa Das and Colleagues (Spectroscopy-base d chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients)
Data on Machine Learning Reported by Bappa Das and Colleagues (Spectroscopy-base d chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Goa, India, by NewsRx editors, research stated, “Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant’s nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemica l analysis when it is to be done over more extensive areas like field- or landsc ape scale.” Our news journalists obtained a quote from the research, “Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-b ased remote sensing or spectroscopy has been explored in the current study to pr edict the foliar nutritional status of the cashew crop. Novel spectral indices ( normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near -infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the f ull dataset) datasets. An independent validation dataset was used for the valida tion of the algorithms tested. The approach of spectral indices yielded very poo r and unreliable predictions for all eleven nutrients. Among the chemometric mod els tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yield ed acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist fo r nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine r egression for calcium, magnesium, iron, copper, and boron; elastic net for sulph ur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micr o-nutrients.”
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