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| 1 | Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry,fractal dimension and visible/near infrared spectroscopy显示文摘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. | Jinru Xue Sigfredo Fuentes Carlos Poblete-Echeverria Claudia Gonzalez Viejo Eden Tongson Hejuan Du Baofeng Su | 2019 | International Journal of Agricultural and Biological Engineering2019，12，2： | 3 |
| 2 | Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies显示文摘Accurate data acquisition and analysis to obtain crop canopy information are critical steps to understand plant growth dynamics and to assess the potential impacts of biotic or abiotic stresses on plant development.A versatile and easy to use monitoring system will allow researchers and growers to improve the follow-up management strategies within farms once potential problems have been detected.This study reviewed existing remote sensing platforms and relevant information applied to crops and specifically grapevines to equip a simple Unmanned Aerial Vehicle(UAV)using a visible high definition RGB camera.The objective of the proposed Unmanned Aerial System(UAS)was to implement a Digital Surface Model(DSM)in order to obtain accurate information about the affected or missing grapevines that can be attributed to potential biotic or abiotic stress effects.The analysis process started with a three-dimensional(3D)reconstruction from the RGB images collected from grapevines using the UAS and the Structure from Motion(SfM)technique to obtain the DSM applied on a per-plant basis.Then,the DSM was expressed as greyscale images according to the halftone technique to finally extract the information of affected and missing grapevines using computer vision algorithms based on canopy cover measurement and classification.To validate the automated method proposed,each grapevine row was visually inspected within the study area.The inspection was then compared to the digital assessment using the proposed UAS in order to validate calculations of affected and missing grapevines for the whole studied vineyard.Results showed that the percentage of affected and missing grapevines was 9.5%and 7.3%,respectively from the area studied.Therefore,for this specific study,the abiotic stress that affected the experimental vineyard(frost)impacted a total of 16.8%of plants.This study provided a new method for automatically surveying affected or missing grapevines in the field and an evaluation tool for plant growth conditions,which can be implemented for other uses such as canopy management,irrigation scheduling and other precision agricultural applications. | Su Baofeng Xue Jinru Xie Chunyu Fang Yulin Song Yuyang Sigfredo Fuentes | 2016 | International Journal of Agricultural and Biological Engineering2016，9，6： | 3 |
| 3 | Responses of leaf night transpiration to drought stress in Vitis vinifera L.显示文摘 | José Mariano Escalona Sigfredo Fuentes Magdalena Tomás Sebastià Martorell Jaume Flexas Hipólito Medrano | 2013 | Agricultural Water Management2013，，： | 1 |
| 4 | Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery显示文摘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(fc).By applying differential fc 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. | Jinru Xue Yeman Fan Baofeng Su Sigfredo Fuentes | 2019 | International Journal of Agricultural and Biological Engineering2019，12，1： | 1 |
| 5 | Responses of lead night transpiration to drought stress in Vitis vinifera L 显示文摘 | JOSE M E F SIGFREDO T MAGDALENA M | 2013 | Agricultural Water Management2013，118，： | 1 |
| 6 | Residents’ attitudes towards an instant resort enclave显示文摘 | Sigfredo A Hernandez Judy Cohen | 1996 | Annals of Tourism Research1996，，4： | 1 |
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