AIMC Topic: Plant Breeding

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The estimation and interpretation of ordered logit models for assessing the factors connected with the productivity of Holstein-Friesian dairy cows in Egypt.

Tropical animal health and production Oct 15, 2022
The incorporation of novel technologies such as artificial intelligence, data mining, and advanced statistical methodologies have received wide responses from researchers. This study was designed to model the factors impacting the actual milk yield o...
Animals Cattle Lactation Logistic Models Artificial Intelligence Egypt Female Milk Plant Breeding
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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant Sep 7, 2022
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...
Genotype Selection, Genetic Genome Plant Breeding Artificial Intelligence Phenotype Big Data Genomics
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High-throughput image-based plant stand count estimation using convolutional neural networks.

PloS one Jul 28, 2022
The landscape of farming and plant breeding is rapidly transforming due to the complex requirements of our world. The explosion of collectible data has started a revolution in agriculture to the point where innovation must occur. To a commercial orga...
Zea mays Plant Breeding Algorithms Phenotype Neural Networks, Computer
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The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM.

Molecules (Basel, Switzerland) Jun 25, 2022
Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such a...
Support Vector Machine Seeds Plant Breeding Algorithms Spectroscopy, Near-Infrared
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Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery.

Sensors (Basel, Switzerland) May 12, 2022
The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approach...
Gossypium Plant Breeding Deep Learning
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Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging.

Scientific reports Feb 24, 2022
Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive a...
Plant Breeding Machine Learning Image Processing, Computer-Assisted Edible Grain Cloud Computing
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Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

PloS one Nov 29, 2021
The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant bre...
Genotype Phenotype Plant Breeding Artificial Intelligence Quantitative Trait Loci Genetic Linkage Computer Simulation Machine Learning
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Combining novel technologies with interdisciplinary basic research to enhance horticultural crops.

The Plant journal : for cell and molecular biology Nov 10, 2021
Horticultural crops mainly include fruits, vegetables, ornamental trees and flowers, and tea trees (Melaleuca alternifolia). They produce a variety of nutrients for the daily human diet in addition to the nutrition provided by staple crops, and some ...
Computational Biology Bioengineering Artificial Intelligence Nutrients Trees Vegetables Plant Breeding Interdisciplinary Research Climate Change Horticulture Flowers Fruit Crops, Agricultural
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Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods.

Sensors (Basel, Switzerland) Nov 9, 2021
Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top ...
Neural Networks, Computer Plant Breeding Plant Leaves Edible Grain Image Processing, Computer-Assisted
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Advances in for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence.

International journal of molecular sciences Sep 29, 2021
Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely ...
Fabaceae Plant Breeding Crops, Agricultural Artificial Intelligence Stress, Physiological Genomics Quantitative Trait Loci
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