一种基于颜色的非破坏性方法在田间条件下测定哈斯鳄梨叶片氮水平

IF 6.3 Q1 AGRICULTURAL ENGINEERING
Ángeles Gallegos , Mayra E. Gavito , Heberto Ferreira-Medina , Eloy Pat , Marta Astier , Sergio Rogelio Tinoco-Martínez , Yair Merlín-Uribe , Carlos E. González-Esquivel
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引用次数: 0

摘要

通过为生产者提供负担得起的支持工具来持续监测营养水平,可以避免牛油果树过度施肥。已经为谷物制作了叶子颜色指南,可能很有用,但到目前为止,它们对树木来说还很少见,因为树木的颜色变化很少。我们研究了叶片颜色指示鳄梨叶片中氮和磷水平的潜力,以开发一种不需要昂贵的化学分析的监测工具。我们进行了三个阶段的实验,以开发一种可靠的、可重复的监测工具。在第一阶段,我们发现颜色与化学测量的N水平之间有很好的关系,但与P水平无关。这使我们能够仅为N个关卡开发叶子颜色图表。在第二阶段,我们使用印刷版和手机应用版本测试了这个视觉指南。我们发现,无论用于检测的材料如何,用户对N水平的视觉识别都是高度可变的、主观的,并且容易出错。第三阶段的目标是开发一种独立于用户的叶子颜色评估方法,使用叶子图片来定义叶子的N级。使用机器和深度学习算法生成、校准和验证模型,利用在野外条件下捕获的数字图像来估计鳄梨叶片的N浓度。利用生成的模型,我们现在可以开发一种用于移动应用程序的自动颜色检测和N水平识别工具,这将帮助鳄梨生产商充分施用氮肥,节省资金并减少果园浸出造成的N污染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of a color-based, non-destructive method to determine leaf N levels of Hass avocado under field conditions

Development of a color-based, non-destructive method to determine leaf N levels of Hass avocado under field conditions
Excessive fertilization in avocado trees might be avoided by providing producers with affordable supporting tools for constant monitoring of nutrient levels. Leaf color guides have been produced for cereals and might be useful, but they are so far rare for trees because of low variation in color. We investigated the potential of leaf color to indicate N and P levels in avocado leaves to develop a monitoring tool not requiring expensive chemical analyses. We carried out three experimental phases towards the development of a solid, reproducible monitoring tool. In the first phase, we found a good relation between color and chemically-measured N levels, but not P levels. That allowed us to develop a leaf color chart only for N levels. In the second phase, this visual guide was tested using print and mobile app versions. We found that visual identification of N levels by the users was highly variable, subjective, and prone to error regardless of the materials used for detection. The third phase aimed to develop a user-independent evaluation of leaf color to define the leaf N level using leaf pictures. Machine and deep learning algorithms were used to generate, calibrate, and validate models for estimating the N concentration of avocado leaves using digital images captured in field conditions. Applying the models generated, we can now develop an automated color detection and N-level identification tool for mobile applications that will assist avocado producers in adequate application of nitrogen fertilizers, saving money and reducing N pollution from leaching in orchards.
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