AIMC Topic: Zea mays

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Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.

Sensors (Basel, Switzerland) May 26, 2024
Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolu...
Deep Learning Image Processing, Computer-Assisted Crops, Agricultural Semantics Zea mays Neural Networks, Computer Phenotype
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The use of image analysis to study the effect of moisture content on the physical properties of grains.

Scientific reports May 22, 2024
Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture cont...
Edible Grain Image Processing, Computer-Assisted Seeds Neural Networks, Computer Zea mays Water
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TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Plant communications May 15, 2024
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target...
Plant Breeding Oryza Crops, Agricultural Zea mays Triticum Neural Networks, Computer Phenotype Genotype Machine Learning
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Carcass traits and morphometry, typification of the Longissimus dorsi muscle and non-carcass components of hair lambs: can biscuit bran completely replace corn? A machine learning approach.

Tropical animal health and production May 13, 2024
Biscuit bran (BB) is a co-product with worldwide distribution, with Brazil as the second largest cookie producer in the world with 1,157,051 tons. We evaluate the impact of completely replacing corn with BB on the characteristics and morphometry of c...
Zea mays Body Composition Animal Feed Brazil Meat Red Meat Sheep, Domestic Machine Learning Male Diet Animals
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Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques.

Scientific reports May 7, 2024
Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared...
Machine Learning Spectroscopy, Near-Infrared Food Contamination Glycine max Macau Zea mays Spectrophotometry Powders Flour
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Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

Scientific reports May 3, 2024
The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize le...
Zea mays Plant Diseases Algorithms Fuzzy Logic Plant Leaves Support Vector Machine
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Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Environmental monitoring and assessment May 2, 2024
Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause eco...
Machine Learning India Agriculture Oryza Crops, Agricultural Floods Environmental Monitoring Zea mays
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Deep learning the cis-regulatory code for gene expression in selected model plants.

Nature communications Apr 25, 2024
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...
Genome, Plant Gene Expression Regulation, Plant Regulatory Sequences, Nucleic Acid Deep Learning Arabidopsis Solanum lycopersicum Sorghum Zea mays Genetic Variation Species Specificity
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Application of a U-Net Neural Network to the Maize Pathosystem.

Phytopathology Apr 22, 2024
Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can presen...
Plant Diseases Puccinia Machine Learning Neural Networks, Computer Plant Leaves Zea mays
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Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 28, 2024
This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral re...
Zea mays Agriculture Spectroscopy, Near-Infrared Proteins Deep Learning
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