Lettuce (Lactuca sativa L.) yield prediction under water stress using artificial neural network (ANN) model and vegetation indices
Lettuce (Lactuca sativa L.) yield prediction under water stress using artificial neural network (ANN) model and vegetation indices
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发表时间：
2012
影响因子：
0.9
通讯作者：
Ü. Kizil;L. Genc;Melis Inalpulat;Duygu Şapolyo;M. Mirik
中科院分区：
文献类型：
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作者：
Ü. Kizil;L. Genc;Melis Inalpulat;Duygu Şapolyo;M. Mirik
水分胁迫是世界各地农作物生产最重要的生长限制因素之一。植物需要水来进行养分吸收、光合作用和呼吸等重要过程。有多种方法可以评估水分胁迫对植物的影响。多年来，一种有前途且普遍采用的压力检测方法是使用遥感提供的信息。遥感和其他非破坏性技术的应用可以实现蔬菜的早期空间胁迫检测。早期胁迫检测对于应用管理实践和最大限度地提高精准农业的最佳产量至关重要。因此，本研究的目的是 1) 确定水分胁迫对不同浇水方式下生长的生菜 (Lactuca sativa L.) 的影响，2) 探索人工神经网络 (ANN) 技术使用光谱植被指数估计生菜产量的性能。使用归一化植被指数（NDVI）、绿色NDVI、红色NDVI、简单比率（SR）、叶绿素绿（CLg）和叶绿素红边（CLr）指数。该研究在体外条件下以三个灌溉水平、四次重复和重复树木时间进行。施加于盆的不同灌溉水平为盆水容量的33%、66%和100%（对照）。灌溉后通过手持式光谱辐射计进行光谱测量。灌溉水的减少导致植物高度、植物直径、每株植物的叶子数量和产量减少。使用前馈、反向传播 ANN 模型中的所有指数提供了最佳预测，对于 100%、66% 和 33% 的水处理，R 2 值分别为 0.86、0.75 和 0.92。总体结果表明，光谱数据和人工神经网络具有预测缺水生菜产量的巨大潜力。
Water stress is one of the most important growth limiting factors in crop production around the world. Water in plants is required to permit vital processes such as nutrient uptake, photosynthesis, and respiration. There are several methods to evaluate the effect of water stress on plants. A promising and commonly practiced method over the years for stress detection is to use information provided by remote sensing. The adaptation of remote sensing and other non-destructive techniques could allow for early and spatial stress detection in vegetables. Early stress detection is essential to apply management practices and to maximize optimal yield for precision farming. Therefore, this study was conducted to 1) determine the effect of water stress on lettuce (Lactuca sativa L.) grown under different watering regime and 2) explore the performance of the artificial neural network (ANN) technique to estimate the lettuce yield using spectral vegetation indices. Normalized difference vegetation index (NDVI), green NDVI, red NDVI, simple ratio (SR), chlorophyll green (CLg), and chlorophyll red edge (CLr) indices were used. The study was carried out in vitro conditions at three irrigation levels with four replicates and repeated tree times. The different irrigation levels applied to the pots were 33, 66 and 100 % (control) of pot water capacity. Spectral measurements were made by a hand-held spectroradiometer after the irrigation. Decrease in irrigation water resulted in reduction in plant height, plant diameter, number of leaves per plant, and yield. Using all indices in a feed-forward, back-propagated ANNs model provided the best prediction with R 2 values of 0.86, 0.75, and 0.92 for 100, 66, and 33 % water treatments, respectively. The overall results indicated that spectral data and ANNs have high potential to predict the lettuce yield exposed to water deficiency .