Johansen Kasper, Morton Mitchell J L, Malbeteau Yoann, Aragon Bruno, Al-Mashharawi Samer, Ziliani Matteo G, Angel Yoseline, Fiene Gabriele, Negrão Sónia, Mousa Magdi A A, Tester Mark A, McCabe Matthew F
Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Front Artif Intell. 2020 May 8;3:28. doi: 10.3389/frai.2020.00028. eCollection 2020.
Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species () through both field and unmanned aerial vehicle (UAV)-based phenotyping of 600 control and 600 salt-treated plants. The study objective was to predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest based on a range of variables derived from the UAV imagery. UAV-based red-green-blue (RGB) imageries collected 1, 2, 4, 6, 7, and 8 weeks before harvest were also used to determine if prediction accuracies varied between control and salt-treated plants. Multispectral UAV-based imagery was also collected 1 and 2 weeks prior to harvest to further explore predictive insights. In order to estimate the end of season biomass and yield, a random forest machine learning approach was implemented using UAV-imagery-derived predictors as input variables. Shape features derived from the UAV, such as plant area, border length, width, and length, were found to have the highest importance in the predictions, followed by vegetation indices and the entropy texture measure. The multispectral UAV imagery collected 2 weeks prior to harvest produced the highest explained variances for fresh shoot mass (87.95%), fruit numbers (63.88%), and yield mass per plant (66.51%). The RGB UAV imagery produced very similar results to those of the multispectral UAV dataset, with the explained variance reducing as a function of increasing time to harvest. The results showed that predicting the yield of salt-stressed plants produced higher accuracies when the models excluded control plants, whereas predicting the yield of control plants was not affected by the inclusion of salt-stressed plants within the models. This research demonstrates that it is possible to predict the average biomass and yield up to 8 weeks prior to harvest within 4.23% of field-based measurements and up to 4 weeks prior to harvest at the individual plant level. Results from this work may be useful in providing guidance for yield forecasting of healthy and salt-stressed tomato plants, which in turn may inform growing practices, logistical planning, and sales operations.
生物量和产量是评估农业系统生产和性能的关键变量。在农场尺度上对单株植物的生物量和产量进行建模和预测是精准农业中的一项重大挑战，尤其是当盐度和其他非生物胁迫可能起作用时。在这里，我们通过对600株对照植物和600株盐处理植物进行田间和基于无人机（UAV）的表型分析，评估了野生番茄物种的一个多样性面板。研究目的是根据从无人机图像中得出的一系列变量，预测收获时的地上部鲜质量、番茄果实数量和产量质量。还使用了在收获前1、2、4、6、7和8周收集的基于无人机的红-绿-蓝（RGB）图像，以确定对照植物和盐处理植物之间的预测准确性是否存在差异。在收获前1周和2周还收集了基于无人机的多光谱图像，以进一步探索预测见解。为了估计季末生物量和产量，采用了随机森林机器学习方法，将无人机图像衍生的预测因子作为输入变量。发现从无人机得出的形状特征，如植物面积、边界长度、宽度和长度，在预测中具有最高的重要性，其次是植被指数和熵纹理测量。收获前2周收集的多光谱无人机图像对地上部鲜质量（87.95%）、果实数量（63.88%）和单株产量质量（66.51%）的解释方差最高。RGB无人机图像产生的结果与多光谱无人机数据集的结果非常相似，随着收获时间的增加，解释方差减小。结果表明，当模型排除对照植物时，预测盐胁迫植物的产量具有更高的准确性，而预测对照植物的产量不受模型中包含盐胁迫植物的影响。这项研究表明，在收获前8周内，可以预测平均生物量和产量，预测结果与基于田间测量的结果相差4.23%以内，在收获前4周内可以在单株水平上进行预测。这项工作的结果可能有助于为健康和盐胁迫番茄植株的产量预测提供指导，进而为种植实践、物流规划和销售运营提供参考。
