Ariza Andrés Aguilar, Sotta Naoyuki, Fujiwara Toru, Guo Wei, Kamiya Takehiro
Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.
Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midoricho, Nishitokyo-shi, Tokyo 188-0002, Japan.
Plant Phenomics. 2024 Jan 29;6:0146. doi: 10.34133/plantphenomics.0146. eCollection 2024.
Recent years have seen the development of novel, rapid, and inexpensive techniques for collecting plant data to monitor the nutritional status of crops. These techniques include hyperspectral imaging, which has been widely used in combination with machine learning models to predict element concentrations in plants. When there are multiple elements, the machine learning models are trained with spectral features to predict individual element concentrations; this type of single-target prediction is known as single-target regression. Although this method can achieve reliable accuracy for some elements, there are others that remain less accurate. We aimed to improve the accuracy of element concentration predictions by using a multi-target regression method that sequentially augmented the original input features (hyperspectral imaging) by chaining the predicted element concentration values. To evaluate the multi-target method, the concentrations of 17 elements in tomato leaves were predicted and compared with the single-target regression results. We trained 5 machine learning models with hyperspectral data and predicted element concentration values and found a significant improvement in the prediction accuracy for 10 elements (Mg, P, S, Mn, Fe, Co, Cu, Sr, Mo, and Cd). Furthermore, our multi-target regression method outperformed single-target predictions by increasing the coefficient of determination () for elements such as Mn, Cu, Co, Fe, and Mg by 12.5%, 10.3%, 11%, 10%, and 8.4%, respectively. Hence, our multi-target method can improve the accuracy of predicting 10-element concentrations compared to single-target regression.
近年来，出现了新颖、快速且廉价的植物数据收集技术，用于监测作物的营养状况。这些技术包括高光谱成像，它已被广泛用于与机器学习模型相结合，以预测植物中的元素浓度。当存在多种元素时，机器学习模型通过光谱特征进行训练，以预测单个元素的浓度；这种单目标预测类型被称为单目标回归。虽然这种方法对某些元素可以实现可靠的准确性，但其他一些元素的准确性仍然较低。我们旨在通过使用多目标回归方法来提高元素浓度预测的准确性，该方法通过将预测的元素浓度值链接起来，依次增强原始输入特征（高光谱成像）。为了评估多目标方法，我们预测了番茄叶片中17种元素的浓度，并与单目标回归结果进行了比较。我们用高光谱数据训练了5个机器学习模型，预测了元素浓度值，发现10种元素（镁、磷、硫、锰、铁、钴、铜、锶、钼和镉）的预测准确性有显著提高。此外，我们的多目标回归方法在预测锰、铜、钴、铁和镁等元素时，通过分别将决定系数（）提高12.5%、10.3%、11%、10%和8.4%，优于单目标预测。因此，与单目标回归相比，我们的多目标方法可以提高10种元素浓度预测的准确性。
