High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images

Francisco Pinto Matthew Paul Reynolds Robert Furbank (2024, [Artículo])

Deep Learning Object-Based Image Analysis Optical Imagery CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE IMAGE ANALYSIS PLANT BREEDING REMOTE SENSING MACHINE LEARNING

Remote sensing of quality traits in cereal and arable production systems: A review

Zhenhai  Li xiuliang jin Gerald Blasch James Taylor (2024, [Artículo])

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Quality Traits Grain Protein CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING QUALITY GRAIN PROTEINS CEREALS PRODUCTION SYSTEMS

Tallo: A global tree allometry and crown architecture database

Tommaso Jucker Jörg Fischer Jerome Chave David Coomes John Caspersen Arshad Ali Grace Jopaul Loubota Panzou Ted R. Feldpausch Daniel Falster Vladimir Andreevich Usoltsev Stephen Adu-Bredu Luciana Alves Mohammad Aminpour Bhely ANGOBOY Ilondea Niels Anten Cécile Antin yousef askari Rodrigo Muñoz Ayyappan Narayanan Patricia Balvanera Lindsay Banin Nicolas Barbier John J. Battles Hans Beeckman Yannick Enock Bocko Benjamin Bond_Lamberty Frans Bongers Samuel Bowers THOMAS BRADE Michiel van Breugel ARTHUR CHANTRAIN Rajeev Chaudhary JINGYU DAI Michele Dalponte Kangbéni Dimobe jean-christophe domec Jean-Louis Doucet Remko Duursma Moisés Enriquez KARIN Y. VAN EWIJK WILLIAM FARFAN_RIOS Adeline FAYOLLE ERIC FORNI David Forrester Hammad Gilani John Godlee Sylvie Gourlet-Fleury Matthias Haeni Jefferson Hall Jie He Andreas Hemp JOSE LUIS HERNANDEZ STEFANONI Steven Higgins ROBERT J. HOLDAWAY Kiramat Hussain Lindsay Hutley Tomoaki Ichie Yoshiko Iida Hai Jiang Puspa Raj Joshi Seyed Hasan Kaboli Maryam Kazempour Larsary Tanaka Kenzo Brian Kloeppel Takashi Kohyama Suwash Kunwar Shem Kuyah Jakub Kvasnica Siliang Lin Emily Lines Hongyan Liu CRAIG LORIMER Joel Loumeto Yadvinder Malhi Peter Marshall Eskil Mattsson Radim Matula Jorge Arturo Meave del Castillo Sylvanus Mensah XIANGCHENG MI Stephane MOMO Takoudjou Glenn Moncrieff Francisco Mora Sarath Nissanka Kevin O'Hara steven pearce Raphaël Pélissier Pablo Luis Peri Pierre Ploton Lourens Poorter mohsen javanmiri pour Hassan pourbabaei JUAN MANUEL DUPUY RADA Sabina Ribeiro Ryan Casey ANVAR SANAEI Jennifer Sanger Michael Schlund Giacomo Sellan Alexander Shenkin Bonaventure Sonké Frank Sterck Martin Svatek Kentaro Takagi Anna Trugman Farman Ullah Matthew Vadeboncoeur Ahmad Valipour Mark Vanderwel Alejandra Vovides Weiwei WANG Li Qiu Christian Wirth MURRAY WOODS Wenhua Xiang Fabiano de Aquino Ximenes Yaozhan Xu TOSHIHIRO YAMADA Miguel A. Zavala (2022, [Artículo])

Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

ALLOMETRIC SCALING CROWN RADIUS FOREST BIOMASS STOCKS FOREST ECOLOGY REMOTE SENSING STEM DIAMETER TREE HEIGHT BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Modeling spatiotemporal patterns of land use/land cover change in Central Malawi using a neural network model

Leah Mungai Joseph Messina Leo Zulu Jiaguo Qi Sieglinde Snapp (2022, [Artículo])

Multilayer Perceptrons CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE LAND USE POPULATION SATELLITE IMAGERY TEXTURE LAND COVER NEURAL NETWORKS REMOTE SENSING

2022 Advanced wheat improvement course: Global wheat rust surveillance

&

monitoring

David Hodson (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS DISEASE SURVEILLANCE NEW TECHNOLOGY REMOTE SENSING

Global rust monitoring

David Hodson (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS DISEASE SURVEILLANCE REMOTE SENSING NEW TECHNOLOGY

KARC 2020 Experiment – The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Gerald Blasch (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING WHEAT CROPS DISEASES

Wheat rust early warning systems in Ethiopia: implementation of remote sensing technologies to combat crop disease

Gerald Blasch (2020, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS MONITORING DISEASE SURVEILLANCE EARLY WARNING SYSTEMS REMOTE SENSING

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions.

Razieh Pourdarbani Sajad Sabzi Mario Hernández Hernández José Luis Hernández-Hernández Ginés García_Mateos Davood Kalantari José Miguel Molina Martínez (2019, [Artículo])

Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most e

ective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.

remote sensing in agriculture artificial neural network hybridization environmental conditions majority voting plum segmentation INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2024, [Artículo])

Common Rust Rp1 Locus CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS REMOTE SENSING VEGETATION INDEX MAIZE CHROMOSOME MAPPING