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

Genetic diversity and population structure of African sorghum (sorghum bicolor L. moench) accessions assessed through single nucleotide polymorphisms markers

Hussein Shimelis Baloua Nébié Chris Ojiewo Abhishek Rathore (2023, [Artículo])

Heterotic Grouping Breeding Population Development Marker-Assisted Cultivar Development CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA POPULATION STRUCTURE GENE FLOW SINGLE NUCLEOTIDE POLYMORPHISMS SORGHUM BICOLOR BREEDING PROGRAMMES

Population genetic structure of the maize weevil, Sitophilus zeamais, in southern Mexico

Michael Jones Martha Willcox (2023, [Artículo])

Maize Weevil Genetic Structure CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURAL WORKERS FILTRATION GENE FLOW MAIZE SINGLE NUCLEOTIDE POLYMORPHISM SITOPHILUS ZEAMAIS CURCULIONIDAE

Difusión de cursos que la Fundación Carlos Slim ofrece en aprende.org

Cesar Petroli (2021, [Poster de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TRAINING AGRICULTURAL TRAINING SOCIAL NETWORKS TRAINING COURSES SUSTAINABLE AGRICULTURE

E. African spring wheat breeding pipeline and Network (CIMMYT-KALRO)

sridhar bhavani (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT BREEDING RESEARCH NETWORKS

The contribution of strong and weak ties to resilience: The case of small-scale maize farming systems in Mexico

Tania Carolina Camacho Villa Ernesto Adair Zepeda Villarreal Julio Díaz-José Roberto Rendon-Medel Bram Govaerts (2023, [Artículo])

Social Network Analysis Farm Typologies Social Ties Strong Ties CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INNOVATION NETWORKS PERSISTENCE SOCIAL NETWORK ANALYSIS MAIZE FARMING SYSTEMS

Chapter 9. Genome-informed discovery of genes and framework of functional genes in wheat

awais rasheed Rudi Appels (2024, [Capítulo de libro])

Wheat Genomics KASP Markers Gene Discovery Functional Markers Gene Networks CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENOMICS SINGLE NUCLEOTIDE POLYMORPHISMS FUNCTIONAL GENOMICS

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Sorghum value chain analysis in semi-arid Zimbabwe

Abbyssinia Mushunje Munyaradzi Junia Mutenje Charles Pfukwa (2019, [Artículo])

Small Scale Farmers Extension Networks CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRO-INDUSTRIAL SECTOR MARKETING MARGINS SORGHUM VALUE CHAINS

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.

Ali Mirzazadeh Afshin Azizi Yousef Abbaspour_Gilandeh José Luis Hernández-Hernández Mario Hernández Hernández Iván Gallardo Bernal (2021, [Artículo])

Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.

rapeseed classification damaged crops deep neural networks INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS