Image-Based High-Throughput Phenotyping in Horticultural Crops
暂无分享,去创建一个
[1] Jesper Cairo Westergaard,et al. In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging , 2023, Comput. Electron. Agric..
[2] D. Lamb,et al. Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery , 2023, Remote. Sens..
[3] Gangshan Wu,et al. Early identification of strawberry leaves disease utilizing hyperspectral imaging combing with spectral features, multiple vegetation indices and textural features , 2023, Comput. Electron. Agric..
[4] Siying Tu,et al. Identification of Chilling Injury in Kiwifruit Using Hyperspectral Structured-Illumination Reflectance Imaging System (SIRI) with Support Vector Machine (SVM) Modelling , 2022, Analytical Letters.
[5] S. Wibowo,et al. Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging , 2022, Applied Sciences.
[6] M. Kim,et al. Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants , 2022, Applied Sciences.
[7] M. Gore,et al. Predicting starch content in cassava fresh roots using near-infrared spectroscopy , 2022, Frontiers in Plant Science.
[8] I. Lizarazo,et al. Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines , 2022, Smart Agricultural Technology.
[9] M. Kim,et al. Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images , 2022, Sensors.
[10] Guijun Yang,et al. Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression , 2022, Remote. Sens..
[11] Guijun Yang,et al. Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs , 2022, Remote. Sens..
[12] A. Solovchenko,et al. Non-Invasive Probing of Winter Dormancy via Time-Frequency Analysis of Induced Chlorophyll Fluorescence in Deciduous Plants as Exemplified by Apple (Malus × domestica Borkh.) , 2022, Plants.
[13] Guijun Yang,et al. Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices , 2022, Frontiers in Plant Science.
[14] A. Abd-Elrahman,et al. Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images , 2022, Plant phenomics.
[15] Guijun Yang,et al. Predicting individual apple tree yield using UAV multi-source remote sensing data and ensemble learning , 2022, Comput. Electron. Agric..
[16] Y. Zhang,et al. Multi-Phenotypic Parameters Extraction and Biomass Estimation for Lettuce Based on Point Clouds , 2022, Social Science Research Network.
[17] A. Abd-Elrahman,et al. Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods , 2022, Remote. Sens..
[18] Tian Qiu,et al. High throughput saliency-based quantification of grape powdery mildew at the microscopic level for disease resistance breeding , 2022, Horticulture Research.
[19] Guijun Yang,et al. Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height , 2022, Frontiers in Plant Science.
[20] Longsheng Fu,et al. Remote estimation of grafted apple tree trunk diameter in modern orchard with RGB and point cloud based on SOLOv2 , 2022, Comput. Electron. Agric..
[21] Deqin Xiao,et al. Remote sensing detection algorithm for apple fire blight based on UAV multispectral image , 2022, Comput. Electron. Agric..
[22] Hak-Jin Kim,et al. Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images , 2022, Sensors.
[23] K. Köhl,et al. LIDAR-Based Phenotyping for Drought Response and Drought Tolerance in Potato , 2022, Potato Research.
[24] J. D. Whyatt,et al. Feasibility of detecting apple scab infections using low-cost sensors and interpreting radiation interactions with scab lesions , 2022, International Journal of Remote Sensing.
[25] J. M. Earles,et al. End-to-end deep learning for directly estimating grape yield from ground-based imagery , 2022, Comput. Electron. Agric..
[26] T. Rath,et al. Detecting low-oxygen stress of stored apples using chlorophyll fluorescence imaging and histogram division , 2022, Postharvest Biology and Technology.
[27] Y. Toda,et al. QTL mapping for seed morphology using the instance segmentation neural network in Lactuca spp , 2022, bioRxiv.
[28] Man Zhang,et al. Banana plant counting and morphological parameters measurement based on terrestrial laser scanning , 2022, Plant methods.
[29] Y. Ge,et al. Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry , 2022, Annals of Forest Science.
[30] Jeffrey C. Berry,et al. A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity , 2022, Plant Methods.
[31] Xiaozeng Yang,et al. Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components , 2022, Plant methods.
[32] Yuan Cheng,et al. Quantitative Extraction and Evaluation of Tomato Fruit Phenotypes Based on Image Recognition , 2022, Frontiers in Plant Science.
[33] A. Abd-Elrahman,et al. Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry. , 2022, Journal of experimental botany.
[34] Minzan Li,et al. Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery , 2022, Remote. Sens..
[35] J. Gago,et al. High-throughput phenotyping of a large tomato collection under water deficit: Combining UAVs’ remote sensing with conventional leaf-level physiologic and agronomic measurements , 2022, Agricultural Water Management.
[36] E. J. de Oliveira,et al. Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction , 2022, PloS one.
[37] M. Zude-Sasse,et al. Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner , 2022, Horticulturae.
[38] M. V. Iersel,et al. Morphological and Physiological Screening to Predict Lettuce Biomass Production in Controlled Environment Agriculture , 2022, Remote. Sens..
[39] Mathew G. Lewsey,et al. Applications of hyperspectral imaging in plant phenotyping. , 2022, Trends in plant science.
[40] Daeil Kim,et al. Bi-dimensional Image Analysis for the Phenotypic Evaluation of Russet in Asian Pear (Pyrus spp.) , 2022, Korean Journal of Horticultural Science and Technology.
[41] Fátima L. Benítez,et al. Determining the Effects of Nanonutrient Application in Cabbage (Brassica oleracea var. capitate L.) Using Spectrometry and Biomass Estimation with UAV , 2021, Agronomy.
[42] Mark E. Everett,et al. Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics , 2021, Remote. Sens..
[43] I. Goldman,et al. Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values , 2021, TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik.
[44] D. Dutta,et al. Near Real Time Monitoring of Potato Late Blight Disease Severity using Field Based Hyperspectral Observation , 2021, Smart Agricultural Technology.
[45] L. Hoagland,et al. Leveraging high-throughput hyperspectral imaging technology to detect cadmium stress in two leafy green crops and accelerate soil remediation efforts. , 2021, Environmental pollution.
[46] Wei Chen,et al. An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment , 2021, Mathematical Problems in Engineering.
[47] Qinghua Lu,et al. Grape Berry Detection and Size Measurement Based on Edge Image Processing and Geometric Morphology , 2021, Machines.
[48] L. Khot,et al. Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique , 2021 .
[49] Dawei Sun,et al. Advances in optical phenotyping of cereal crops. , 2021, Trends in plant science.
[50] R. Ferrarezi,et al. Use of Thermal Imaging to Assess Water Status in Citrus Plants in Greenhouses , 2021, Horticulturae.
[51] P. Verboven,et al. X-ray computed tomography for 3D plant imaging. , 2021, Trends in plant science.
[52] Suha Berberoglu,et al. Computer vision-based citrus tree detection in a cultivated environment using UAV imagery , 2021, Comput. Electron. Agric..
[53] Erin E. Sparks,et al. Image-based assessment of plant disease progression identifies new genetic loci for resistance , 2021, bioRxiv.
[54] Mathew G. Lewsey,et al. Noninvasive imaging technologies in plant phenotyping. , 2021, Trends in Plant Science.
[55] Y. Chung,et al. A short review of RGB sensor applications for accessible high-throughput phenotyping , 2021, Journal of Crop Science and Biotechnology.
[56] I. Goldman,et al. A Digital Image-Based Phenotyping Platform for Analyzing Root Shape Attributes in Carrot , 2021, Frontiers in Plant Science.
[57] Wei Wang,et al. Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices , 2021, Remote. Sens..
[58] Stuart R. Phinn,et al. Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery , 2021, Remote. Sens..
[59] J. Senthilnath,et al. Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform , 2021, Remote. Sens..
[60] Xiaowei Yu,et al. Three dimensional apple tree organs classification and yield estimation algorithm based on multi-features fusion and support vector machine , 2021 .
[61] R. Lu,et al. Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging , 2021, Foods.
[62] Ewa Ropelewska,et al. Cultivar discrimination of stored apple seeds based on geometric features determined using image analysis , 2021 .
[63] Yong He,et al. Recognition of early blight and late blight diseases on potato leaves based on graph cut segmentation , 2021 .
[64] Jorge Torres-Sánchez,et al. Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards , 2021, Sensors.
[65] Majed A. Alotaibi,et al. Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes , 2021, Remote. Sens..
[66] K. Tatsumi,et al. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery , 2021, Plant methods.
[67] Sumit Jangra,et al. High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement , 2021, Phenomics.
[68] U. Sonnewald,et al. X-Ray CT Phenotyping Reveals Bi-Phasic Growth Phases of Potato Tubers Exposed to Combined Abiotic Stress , 2021, Frontiers in Plant Science.
[69] A. Hayashi,et al. Strawberry fruit shape: quantification by image analysis and QTL detection by genome-wide association analysis , 2021, Breeding science.
[70] Ivan Simko,et al. Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning , 2021, Frontiers in Genetics.
[71] M. Musse,et al. A global non-invasive methodology for the phenotyping of potato under water deficit conditions using imaging, physiological and molecular tools , 2021, Plant methods.
[72] Jouko Kleemola,et al. Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation , 2021, Remote. Sens..
[73] Danielle Elis Garcia Furuya,et al. Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery , 2021, Precision Agriculture.
[74] Gabriel Mascarenhas Maciel,et al. High-throughput phenotyping to detect anthocyanins, chlorophylls, and carotenoids in red lettuce germplasm , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[75] M. Dwivedi,et al. Emerging nondestructive technologies for quality assessment of fruits, vegetables, and cereals , 2021 .
[76] Xinyu Guo,et al. Greenhouse-based vegetable high-throughput phenotyping platform and trait evaluation for large-scale lettuces , 2021, Comput. Electron. Agric..
[77] Flavio Prieto,et al. Assessment of potato late blight from UAV-based multispectral imagery , 2021, Comput. Electron. Agric..
[78] Michael Selvaraj,et al. Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner , 2021, Remote. Sens..
[79] Junho Yeom,et al. Unmanned Aircraft System- (UAS-) Based High-Throughput Phenotyping (HTP) for Tomato Yield Estimation , 2021, J. Sensors.
[80] Kanda Runapongsa Saikaew,et al. End-to-End Automatic Berry Counting for Table Grape Thinning , 2021, IEEE Access.
[81] Xiang Li,et al. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field , 2021, Frontiers in Bioengineering and Biotechnology.
[82] John J. Sulik,et al. Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean , 2021, Frontiers in Plant Science.
[83] David Rousseau,et al. Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds , 2020, ArXiv.
[84] Nele Bendel,et al. Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging , 2020, Remote. Sens..
[85] Junho Yeom,et al. Comparison of Canopy Shape and Vegetation Indices of Citrus Trees Derived from UAV Multispectral Images for Characterization of Citrus Greening Disease , 2020, Remote. Sens..
[86] Yuzhen Lu,et al. Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress , 2020 .
[87] Jannat Yasmin,et al. Classification of pepper seed quality based on internal structure using X-ray CT imaging , 2020, Comput. Electron. Agric..
[88] Lav R. Khot,et al. Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging , 2020, Comput. Electron. Agric..
[89] Jannat Yasmin,et al. Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning , 2020, Sensors.
[90] Jingyi Jin,et al. High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography , 2020, Frontiers in Plant Science.
[91] A. Monfort,et al. Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry , 2020, bioRxiv.
[92] Dries Raymaekers,et al. Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin , 2020 .
[93] Xiaozeng Yang,et al. Image-Based High-Throughput Detection and Phenotype Evaluation Method for Multiple Lettuce Varieties , 2020, Frontiers in Plant Science.
[94] Rodrigo Pereira Ramos,et al. Non-Invasive Setup for Grape Maturation Classification using Deep Learning. , 2020, Journal of the science of food and agriculture.
[95] Yi Wang,et al. Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning , 2020, Sensors.
[96] Xin Zhang,et al. Faster R–CNN–based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting , 2020 .
[97] Yuanyuan Shao,et al. Assessment of Strawberry Ripeness Using Hyperspectral Imaging , 2020 .
[98] Yiannis Ampatzidis,et al. Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning , 2020, Remote. Sens..
[99] Zetian Fu,et al. Growth monitoring of greenhouse lettuce based on a convolutional neural network , 2020, Horticulture Research.
[100] Bo Li,et al. Defining strawberry shape uniformity using 3D imaging and genetic mapping , 2020, Horticulture Research.
[101] Samiul Haque,et al. Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery , 2020, bioRxiv.
[102] Zhenhai Li,et al. Extracting apple tree crown information from remote imagery using deep learning , 2020, Comput. Electron. Agric..
[103] Katarzyna Kubiak,et al. Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves , 2020, Remote. Sens..
[104] A. Siquieroli,et al. Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels , 2020, Bragantia.
[105] Zhengang Yang,et al. Visual detection of green mangoes by an unmanned aerial vehicle in orchards based on a deep learning method , 2020, Biosystems Engineering.
[106] Scarlett Liu,et al. A vision-based robust grape berry counting algorithm for fast calibration-free bunch weight estimation in the field , 2020, Comput. Electron. Agric..
[107] Cornelia Weltzien,et al. Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry , 2020, Remote. Sens..
[108] Bruno Aragon,et al. Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest , 2020, Frontiers in Artificial Intelligence.
[109] Benjamin E. Wilkinson,et al. Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images , 2020 .
[110] Vinicius Bitencourt Campos Calou,et al. The use of UAVs in monitoring yellow sigatoka in banana , 2020 .
[111] Michael A Hardigan,et al. Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry , 2020, GigaScience.
[112] S. Delalieux,et al. Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors , 2020, Agronomy.
[113] M. Clark,et al. Evaluating and Mapping Grape Color Using Image-Based Phenotyping , 2020, Plant phenomics.
[114] Fumio Okura,et al. Training instance segmentation neural network with synthetic datasets for crop seed phenotyping , 2020, Communications Biology.
[115] Li Zhang,et al. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging , 2020 .
[116] James Y. Kim. Roadmap to High Throughput Phenotyping for Plant Breeding , 2020, Journal of Biosystems Engineering.
[117] A. Acharjee,et al. Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz) , 2020, Plant Methods.
[118] Nilton Nobuhiro Imai,et al. A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.
[119] Yael Edan,et al. In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera , 2020, IEEE Robotics and Automation Letters.
[120] L. Xiong,et al. Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. , 2020, Molecular plant.
[121] Liang Wan,et al. Characterization and Detection of Leaf Photosynthetic Response to Citrus Huanglongbing from Cool to Hot Seasons in Two Orchards , 2020 .
[122] Jeffrey B. Endelman,et al. Image-based Phenotyping and Genetic Analysis of Potato Skin Set and Color , 2019, bioRxiv.
[123] M. Trtílek,et al. Lettuce reaction to drought stress: automated high-throughput phenotyping of plant growth and photosynthetic performance , 2020 .
[124] Ben Somers,et al. In-field detection of Alternaria solani in potato crops using hyperspectral imaging , 2020, Comput. Electron. Agric..
[125] M. Omirou,et al. Phenotyping and Plant Breeding: Overcoming the Barriers , 2020, Frontiers in Plant Science.
[126] Volker Steinhage,et al. Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches , 2019, Remote. Sens..
[127] Ye Sun,et al. Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging , 2019, Sensors.
[128] Yiannis Ampatzidis,et al. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques , 2019, Precision Agriculture.
[129] Jonathan Li,et al. Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks , 2019, Remote. Sens..
[130] Tony P. Pridmore,et al. Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images , 2019, Front. Plant Sci..
[131] Ivan Simko,et al. Phenomic and Physiological Analysis of Salinity Effects on Lettuce , 2019, Sensors.
[132] Francesco Cellini,et al. Drought phenotyping in Vitis vinifera using RGB and NIR imaging , 2019, Scientia Horticulturae.
[133] Ji Hyeon Kim,et al. Application of maximum quantum yield, a parameter of chlorophyll fluorescence, for early determination of bacterial wilt in tomato seedlings , 2019, Horticulture, Environment, and Biotechnology.
[134] Alexander Wendel,et al. Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation , 2019, Comput. Electron. Agric..
[135] Guoxiang Sun,et al. Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants , 2019, Sensors.
[136] R. Vickerstaff,et al. Identifying Verticillium dahliae Resistance in Strawberry Through Disease Screening of Multiple Populations and Image Based Phenotyping , 2019, Front. Plant Sci..
[137] Marnin D. Wolfe,et al. Improving root characterisation for genomic prediction in cassava , 2019, bioRxiv.
[138] Jeffrey B. Endelman,et al. Image-based Phenotyping and Genetic Analysis of Potato Skin Set and Color , 2019, bioRxiv.
[139] Won Suk Lee,et al. Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages , 2019, Remote. Sens..
[140] Ampatzidis,et al. Evaluation of a Ground Penetrating Radar to Map the Root Architecture of HLB-infected Citrus Trees , 2019, Agronomy.
[141] M. Tester,et al. Breeding crops to feed 10 billion , 2019, Nature Biotechnology.
[142] Yiannis Ampatzidis,et al. UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning , 2019, Remote. Sens..
[143] Yiming Guo,et al. Nondestructive Phenomic Tools for the Prediction of Heat and Drought Tolerance at Anthesis in Brassica Species , 2019, Plant phenomics.
[144] Aakash Chawade,et al. High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture , 2019, Agronomy.
[145] Yongsheng Si,et al. High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple , 2019, Front. Plant Sci..
[146] Ashok Samal,et al. Leveraging Image Analysis for High-Throughput Plant Phenotyping , 2019, Front. Plant Sci..
[147] Bruno Aragon,et al. Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress , 2019, Front. Plant Sci..
[148] Md. Sultan Mahmud,et al. Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection , 2019, Comput. Electron. Agric..
[149] Zhenfen Dong,et al. Chlorophyll fluorescence imaging as a tool for analyzing the effects of chilling injury on tomato seedlings , 2019, Scientia Horticulturae.
[150] G. Urek,et al. Pipeline for imaging, extraction, pre-processing, and processing of time-series hyperspectral data for discriminating drought stress origin in tomatoes , 2019, MethodsX.
[151] Yiannis Ampatzidis,et al. UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence , 2019, Remote. Sens..
[152] J. M. Molina-Martínez,et al. An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video , 2019, Agronomy.
[153] Liping Jin,et al. The estimation of crop emergence in potatoes by UAV RGB imagery , 2019, Plant Methods.
[154] Marston Héracles Domingues Franceschini,et al. Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato , 2019, Remote. Sens..
[155] Haleem Farman,et al. Deep Learning: Convergence to Big Data Analytics , 2018, SpringerBriefs in Computer Science.
[156] Farid Melgani,et al. Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective , 2018, GigaScience.
[157] Yong Suk Chung,et al. Image Analysis for Measuring Disease Symptom to Bacterial Soft Rot in Potato , 2019, American Journal of Potato Research.
[158] J. Pérez-Pérez,et al. Morphological Characterization of Root System Architecture in Diverse Tomato Genotypes during Early Growth , 2018, International journal of molecular sciences.
[159] Alexander Wendel,et al. Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform , 2018, Comput. Electron. Agric..
[160] Nathan D. Miller,et al. An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.) , 2018, Front. Plant Sci..
[161] Maggi Kelly,et al. Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks , 2018, Drones.
[162] Ashutosh Kumar Singh,et al. Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives. , 2018, Trends in plant science.
[163] Julio Martin Duarte-Carvajalino,et al. Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms , 2018, Remote. Sens..
[164] R. Laxman,et al. Non-invasive quantification of tomato (Solanum lycopersicum L.) plant biomass through digital imaging using phenomics platform , 2018 .
[165] Spyros Fountas,et al. Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes , 2018, Agriculture.
[166] Harpuneet Kaur,et al. Evaluation of plum fruit maturity by image processing techniques , 2018, Journal of Food Science and Technology.
[167] Dong-Wook Kim,et al. Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery , 2018, Remote. Sens..
[168] Robert Richter,et al. High-Precision Phenotyping of Grape Bunch Architecture Using Fast 3D Sensor and Automation , 2018, Sensors.
[169] J. F. Ortega,et al. Onion biomass monitoring using UAV-based RGB imaging , 2018, Precision Agriculture.
[170] G. Zizka,et al. Improved non-destructive 2D and 3D X-ray imaging of leaf venation , 2018, Plant Methods.
[171] Ganesh C. Bora,et al. Quantification of browning in apples using colour and textural features by image analysis , 2017 .
[172] A. Novo,et al. Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz) , 2017, Plant Methods.
[173] Ian Stavness,et al. Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks , 2017, Front. Plant Sci..
[174] Hanno Scharr,et al. A cognitive architecture for automatic gardening , 2017, Comput. Electron. Agric..
[175] S. H. Ahmadi,et al. Comparing canopy temperature and leaf water potential as irrigation scheduling criteria of potato in water-saving irrigation strategies , 2017 .
[176] Jianfeng Cheng,et al. A Novel Auto-Sorting System for Chinese Cabbage Seeds , 2017, Sensors.
[177] S. Sankaran,et al. Potato Tuber Length-Width Ratio Assessment Using Image Analysis , 2017, American Journal of Potato Research.
[178] Hanno Scharr,et al. Machine Learning for Plant Phenotyping Needs Image Processing. , 2016, Trends in plant science.
[179] M. Hirafuji,et al. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle , 2016 .
[180] K. Tu,et al. Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. , 2016, Food chemistry.
[181] Hairong Zhang,et al. Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis , 2015, Scientific Reports.
[182] Evelyne Costes,et al. Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit , 2015, Journal of experimental botany.
[183] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[184] C. Glasbey,et al. Automated estimation of leaf area development in sweet pepper plants from image analysis. , 2015, Functional plant biology : FPB.
[185] Ulrich Schurr,et al. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification , 2015, Plant Methods.
[186] Andrew M Mutka,et al. Image-based phenotyping of plant disease symptoms , 2015, Front. Plant Sci..
[187] Qin Zhang,et al. A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.
[188] James W. McNicol,et al. Infra-red Thermography for High Throughput Field Phenotyping in Solanum tuberosum , 2013, PLoS ONE.
[189] David Rousseau,et al. Application note: Thermography versus chlorophyll fluorescence imaging for detection and quantification of apple scab , 2013 .
[190] D. Surya Prabha,et al. Assessment of banana fruit maturity by image processing technique , 2015, Journal of Food Science and Technology.
[191] Francesco De Carlo,et al. X-ray imaging of leaf venation networks. , 2012, The New phytologist.
[192] Thomas Neuberger,et al. Surveying the plant's world by magnetic resonance imaging. , 2012, The Plant journal : for cell and molecular biology.
[193] Thomas Werner,et al. Next generation sequencing in functional genomics , 2010, Briefings Bioinform..
[194] Ning Wang,et al. Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks , 2009 .
[195] Meftah Salem M. Alfatni,et al. Oil palm fruit bunch grading system using red, green and blue digital number. , 2008 .
[196] K Maxwell,et al. Chlorophyll fluorescence--a practical guide. , 2000, Journal of experimental botany.
[197] Charles R. Giardina,et al. Elliptic Fourier features of a closed contour , 1982, Comput. Graph. Image Process..