AIMC Topic: Zea mays

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DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.

Plant communications Jun 10, 2024
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been...
Deep Learning Promoter Regions, Genetic Gene Expression Regulation, Plant Chromatin Zea mays Base Sequence
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Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging.

Journal of hazardous materials May 28, 2024
The misuse of herbicides in fields can cause severe toxicity in maize, resulting in significant reductions in both yield and quality. Therefore, it is crucial to develop early and efficient methods for assessing herbicide toxicity, protecting maize p...
Sulfonylurea Compounds Zea mays Herbicides Deep Learning Pyridines
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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...
Phenotype Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Semantics Zea mays Crops, Agricultural
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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...
Water Neural Networks, Computer Zea mays Image Processing, Computer-Assisted Edible Grain Seeds
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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...
Triticum Oryza Plant Breeding Crops, Agricultural Phenotype Neural Networks, Computer Genotype Machine Learning Zea mays
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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...
Sheep, Domestic Meat Zea mays Animal Feed Red Meat Body Composition Diet Brazil Animals Male Machine Learning
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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...
Spectroscopy, Near-Infrared Food Contamination Zea mays Machine Learning Spectrophotometry Flour Macau Powders Glycine max
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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...
Plant Diseases Fuzzy Logic Plant Leaves Zea mays Algorithms 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...
Crops, Agricultural Floods Machine Learning Zea mays Agriculture India Oryza Environmental Monitoring
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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...
Regulatory Sequences, Nucleic Acid Solanum lycopersicum Deep Learning Gene Expression Regulation, Plant Genome, Plant Zea mays Sorghum Arabidopsis Genetic Variation Species Specificity
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