AIMC Topic: Germination

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Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.

Scientific reports
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneo...

Semi-automated high content analysis of pollen performance using tubetracker.

Plant reproduction
TubeTracker provides a method to partially automate analysis of pollen tube growth using live imaging. Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitati...

Modelling of pome fruit pollen performance using machine learning.

Scientific reports
Agriculture, particularly fruit production, is considered a crucial industry with a significant economic impact in many countries. Extreme fluctuations in air temperature can negatively affect the flowering periods of fruit species. Therefore, it is ...

Machine learning-based prediction of compost maturity and identification of key parameters during manure composting.

Bioresource technology
Evaluating compost maturity, e.g. via manual seed germination index (GI) measurement, is both time-consuming and costly during composting. This study employed six machine learning methods, including random forest (RF), extra tree (ET), eXtreme gradie...

Non-destructive detection strategy of maize seed vigor based on seed phenotyping and the potential for accelerating breeding.

Journal of advanced research
INTRODUCTION: Seeds are fundamental to agricultural production, and their vigor affects seedling quality, quantity, and crop yield. Accurate vigor assessment methods are crucial for agricultural productivity.

Allelopathic effects of six alfalfa varieties at three stubbles on the germination, seedling and root growth of green foxtail and barnyardgrass.

PloS one
Alfalfa (Medicago sativa) is known to release allelopathic substances to affect the germination and growth of other plants, which have the potential to be applied in controlling weeds. Green foxtail (Setaria viridis) and barnyardgrass (Echinochloa cr...

Predicting maturity and identifying key factors in organic waste composting using machine learning models.

Bioresource technology
The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and...

Deep-learning-based automatic evaluation of rice seed germination rate.

Journal of the science of food and agriculture
BACKGROUND: Rice is an important food crop plant in the world and is also a model plant for genetics and breeding research. The germination rate is an important indicator that measures the performance of rice seeds. Currently, solutions involving ima...

Micronutrient seed priming: new insights in ameliorating heavy metal stress.

Environmental science and pollution research international
Plants need to survive with changing environmental conditions, be it different accessibility to water or nutrients, or attack by insects or pathogens. Few of these changes, especially heavy metal stress, can become more stressful and needed strong co...

Robust seed germination prediction using deep learning and RGB image data.

Scientific reports
Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with w...