Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study
Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study
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DOI：
10.3390/rs12223809
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发表时间：
2020-11-01
期刊：
影响因子：
5
通讯作者：
van Aardt, Jan
中科院分区：
文献类型：
--
作者：
Hassanzadeh, Amirhossein;Murphy, Sean P.;van Aardt, Jan
农业受到大量食物浪费的困扰，其中一些浪费是由于无法在农场一级实行针对具体地点的管理。油豆角是一种广泛种植的作物，覆盖了美国数十万英亩的土地，也不能免除对知情的、田间的和空间明确的管理方法的需求。本研究旨在评估机器学习算法在油豆角生长阶段和豆荚成熟度分类中的应用。亨廷顿），以及检测和区分光谱和生物物理特征，从而获得准确的分类结果。四个主要的生长阶段和六个主要的筛荚成熟度水平分别评估的生长阶段和荚成熟度分类。使用可见-近红外和短波-红外域（VNIR-SWIR; 400-2500 nm）中的基于点的原位分光辐射计，并且将辐射值转换为反射率以针对样品之间的任何照明变化进行归一化。在对原始数据进行预处理后，采用多类分类法对荚果成熟度进行评估，采用二元和多类分类法对生育期进行确定。通过二进制方法从生长阶段评估的结果显示出90- 98%的准确度，最好的数学增强方法是连续去除方法。生长阶段多类分类方法使用原始反射率数据，并确定了一对波长，493 nm和640 nm，在两个基本的变换（比率和归一化差异），产生高精度（类似于79%）。豆荚成熟度评估检测到窄带波长的维斯和短波红外区域，分离之间的不准备收获和准备收获的情况下，分类措施在类似的78%的水平，通过使用连续删除的光谱。我们的工作是一个最好的情况下，即，我们认为它是理解经由可缩放级别的高光谱感测的油豆角收获成熟度评估的垫脚石（即，机载系统）。未来的工作涉及将概念转移到无人机系统（UAS）的现场实验，并验证安装在UAS上的简单多光谱相机是否可以包含< 10个光谱波段，以满足生长阶段和豆荚成熟度分类的需要。
The agricultural industry suffers from a significant amount of food waste, some of which originates from an inability to apply site-specific management at the farm-level. Snap bean, a broad-acre crop that covers hundreds of thousands of acres across the USA, is not exempt from this need for informed, within-field, and spatially-explicit management approaches. This study aimed to assess the utility of machine learning algorithms for growth stage and pod maturity classification of snap bean (cv. Huntington), as well as detecting and discriminating spectral and biophysical features that lead to accurate classification results. Four major growth stages and six main sieve size pod maturity levels were evaluated for growth stage and pod maturity classification, respectively. A point-based in situ spectroradiometer in the visible-near-infrared and shortwave-infrared domains (VNIR-SWIR; 400-2500 nm) was used and the radiance values were converted to reflectance to normalize for any illumination change between samples. After preprocessing the raw data, we approached pod maturity assessment with multi-class classification and growth stage determination with binary and multi-class classification methods. Results from the growth stage assessment via the binary method exhibited accuracies ranging from 90-98%, with the best mathematical enhancement method being the continuum-removal approach. The growth stage multi-class classification method used raw reflectance data and identified a pair of wavelengths, 493 nm and 640 nm, in two basic transforms (ratio and normalized difference), yielding high accuracies (similar to 79%). Pod maturity assessment detected narrow-band wavelengths in the VIS and SWIR region, separating between not ready-to-harvest and ready-to-harvest scenarios with classification measures at the similar to 78% level by using continuum-removed spectra. Our work is a best-case scenario, i.e., we consider it a stepping-stone to understanding snap bean harvest maturity assessment via hyperspectral sensing at a scalable level (i.e., airborne systems). Future work involves transferring the concepts to unmanned aerial system (UAS) field experiments and validating whether or not a simple multispectral camera, mounted on a UAS, could incorporate < 10 spectral bands to meet the need of both growth stage and pod maturity classification in snap bean production.