Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.
The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts. More recently, machine learning algorithms for pattern recognition have been successfully applied to leaf recognition in other plant species. These new tools make the classification of Chinese medicinal plants easier, more efficient and cost effective. This study showed comparative results between machine learning models obtained from two methods: i) a morpho-colorimetric method and ii) a visible (VIS)/Near Infrared (NIR) spectral analysis from sampled leaves of 20 different Chinese medicinal plants. Specifically, the automated image analysis and VIS/NIR spectral based parameters obtained from leaves were used separately as inputs to construct customized artificial neural network (ANN) models. Results showed that the ANN model developed using the morpho-colorimetric parameters as inputs (Model A) had an accuracy of 98.3% in the classification of leaves for the 20 medicinal plants studied. In the case of the model based on spectral data from leaves (Model B), the ANN model obtained using the averaged VIS/NIR spectra per leaf as inputs showed 92.5% accuracy for the classification of all medicinal plants used. Model A has the advantage of being cost effective, requiring only a normal document scanner as measuring instrument. This method can be adapted for non-destructive assessment of leaves in-situ by using portable wireless scanners. Model B combines the fast, non-destructive advantages of VIS/NIR spectroscopy, which can be used for rapid and non-invasive identification of Chinese medicinal plants and other applications by analyzing specific light spectra overtones from leaves to assess concentration of pigments such as chlorophyll, anthocyanins and others that are related active compounds from the medicinal plants.
Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development. By implementing a Digital Surface Model (DSM) to imagery obtained using Unmanned Aerial Vehicles (UAV), it is possible to filter canopy information effectively based on height, which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover crops. This paper describes a method based on the DSM to assess canopy growth (CG) as well as missing plants from a kiwifruit orchard on a plant-by-plant scale. The DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion (SfM) algorithm. An adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular surface. Furthermore, a customized algorithm was developed to discriminate single kiwifruit plants automatically, which allowed the estimation of individual canopy cover fractions (f c ). By applying differential f c thresholding, four categories of the CG were determined automatically: (i) missing plants; (ii) low vigor; (iii) moderate vigor; and (iv) vigorous. Results were validated by a detailed visual inspection on the ground, which rendered an overall accuracy of 89.5% for the method proposed to assess CG at the plant-by-plant level. Specifically, the accuracies for CG category (i)-(iv) were 94.1%, 85.1%, 86.7%, and 88.0%, respectively. The proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to be distinguished by common colour thresholding or edge identification methods.
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