Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

中文翻译：

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基于高光谱反射率识别植物不同生长阶段叶片磷含量


出于经济和环境原因，现代农业致力于可持续地管理肥料。监测生长中植物的任何营养（磷、氮、钾）缺乏症是精准农业技术的一个挑战。对三种流行作物进行了研究：芹菜（Apiumgravolens L.，cv.Neon）、甜菜（Beta vulgaris L.，cv.貘）和草莓（Fragaria × ananassa Duchesne，cv.Honeoye），施肥具有四种不同剂量的磷（P），以提供无创检测 P 含量的数据。通过对植物材料中叶绿素和类胡萝卜素含量的生化分析获得的数据表明，与草莓相比，磷对植物有效性的最强影响是甜菜和芹菜中不同的总叶绿素含量，其中磷影响多种类胡萝卜素叶子中的内容物。使用在植物发育的几个不同阶段获得的高光谱成像进行的测量被应用于监督分类实验。开发了一种机器学习算法（反向传播神经网络、随机森林、朴素贝叶斯和支持向量机）来对四种施磷方式的植物进行分类。在植物发育的最早测量阶段获得了最低的预测精度。统计分析显示了叶子生化成分、磷肥和植物叶子/根质量之间的相关性。获得的结果表明，高光谱成像与人工智能方法相结合具有对作物水平非均质磷施肥进行非侵入性检测的潜力。