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Crop yield prediction integrating genotype and weather variables using deep learning.

PloS one
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop producti...

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology
We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (s...

Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time.

Food chemistry
This study aims to investigate the changes in physical and chemical properties of edamame during bean development and apply a spectroscopy-based machine learning (ML) technique to determine optimal harvest time. The edamame harvested at R5 (beginning...

Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.

Nature communications
Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is ofte...

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.

BMC plant biology
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few app...

Predicting the quality of soybean seeds stored in different environments and packaging using machine learning.

Scientific reports
The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optim...

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Sensors (Basel, Switzerland)
Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crop...

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Briefings in bioinformatics
Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. A...

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage.

International journal of molecular sciences
Soybean ( (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide us...

Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method.

Food chemistry
The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention netwo...