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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
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
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
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
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])
Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES
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
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])
Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION
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
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