AIMC Topic: Seeds

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Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.

PloS one Mar 31, 2021
The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with differ...
Regression Analysis Neural Networks, Computer Plant Leaves Algorithms Nitrogen Image Processing, Computer-Assisted Brassica Agriculture Seeds
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Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Feb 14, 2021
How to quickly and accurately select sugarbeet seeds with reliable germination is very important to sugarbeet planting. In this study, the hyperspectral images of 3072 sugarbeet seeds of the same variety were collected, and were successively processe...
Support Vector Machine Seeds Germination Algorithms Image Processing, Computer-Assisted
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Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms.

Sensors (Basel, Switzerland) Dec 7, 2020
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry a...
Hyperspectral Imaging Glycine max Algorithms Support Vector Machine Seeds Machine Learning
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Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning.

Sensors (Basel, Switzerland) Nov 26, 2020
In this study, conventional machine learning and deep leaning approaches were evaluated using X-ray imaging techniques for investigating the internal parameters (endosperm and air space) of three cultivars of watermelon seed. In the conventional mach...
Deep Learning Seeds Citrullus Machine Learning Algorithms X-Rays
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Interactive machine learning for soybean seed and seedling quality classification.

Scientific reports Jul 9, 2020
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on inte...
Glycine max Germination Seedlings False Positive Reactions Seeds Principal Component Analysis Artificial Intelligence Machine Learning Software Algorithms Reproducibility of Results Image Processing, Computer-Assisted
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Morphological traits of drought tolerant horse gram germplasm: classification through machine learning.

Journal of the science of food and agriculture Jul 3, 2020
BACKGROUND: Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with good drought resistance traits. It is a rich source of protein. Conventional breeding methods for high yielding and abiotic stress tolerant germplasm are...
Stress, Physiological Quantitative Trait Loci Droughts Seeds Fabaceae Plant Breeding Machine Learning Algorithms Phenotype Genotype
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Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.

Analytical biochemistry May 28, 2020
Lysine 2-hydroxyisobutyrylation (K) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of K sites is a first...
Machine Learning Seeds Protein Processing, Post-Translational Oryza HeLa Cells Humans Species Specificity Lysine Hydroxybutyrates Bryopsida Saccharomyces cerevisiae
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Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

Communications biology Apr 15, 2020
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segme...
Neural Networks, Computer Phenotype Deep Learning Crops, Agricultural Hordeum Image Processing, Computer-Assisted Seeds
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Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques.

Journal of the science of food and agriculture Nov 21, 2019
BACKGROUND: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG an...
Machine Learning Algorithms Image Processing, Computer-Assisted Neural Networks, Computer Artificial Intelligence Seeds Triticum Turkey
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Discovery of food identity markers by metabolomics and machine learning technology.

Scientific reports Jul 4, 2019
Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to...
Metabolome Food Handling Sesamum Flax Salvia Humans Biomarkers Machine Learning Food Analysis Seeds
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