摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx编辑在印度果阿的新闻报道，研究表明，“果园作物的精确营养管理需要精确、准确和实时的植物营养状况信息。这受到了这样一个事实的限制，即当它要在更广泛的地区，如田地或陆地猿规模上进行时，需要大量的叶子取样和化学分析。”我们的新闻记者从这项研究中获得了一句话：“因此，需要快速、可靠和可重复的养分估算方法。在此背景下，本研究探索了基于实验室的遥感或光谱技术，以揭示腰果作物叶片的营养状况。新的光谱指数(归一化差和简单比)，化学计量学建模，化学计量学模型。”利用可见近红外高光谱数据的偏最小二乘回归(PLSR)组合机器学习模型对腰果叶片宏量和微量元素含量进行预测,将整个数据集分为校准(70%全数据集)和验证(30%全数据集)两部分,并使用一个独立的验证数据集对算法进行验证。PLSR结合机器学习建模方法对除硫和铜外的所有营养元素的预测效果都很好,且预测结果不可靠,PLSR结合立体图对氮、磷、钾、磷目前的研究表明，基于高光谱遥感的模型可以用于腰果叶片宏观和微观营养的无损和快速估计。
Abstract
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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