AIMC Topic: Gossypium

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Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potenti...

Detection of commercial crop weeds using machine learning algorithms.

Scientific reports
This work investigates the YOLOv5 object detection algorithms for classifying commercial crops such as tomatoes, chili, and cotton. The data sets comprise 707 images of green chillies, 200 images of tomato crops and 130 images of weeds from Ponnandag...

Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture.

Scientific reports
Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different ty...

Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models.

Scientific reports
Cotton, the backbone of global textile production, demands sustainable agricultural practices to ensure fiber, food, and environmental security. Cotton crop play an essential role in farming economies; however, production is sometimes affected by var...

Detection of cotton crops diseases using customized deep learning model.

Scientific reports
The agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, c...

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic archit...

Detection of Verticillium infection in cotton leaves using ATR-FTIR spectroscopy coupled with machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Verticillium wilt (VW) is a soil-borne vascular disease that affects upland cotton and is caused by Verticillium dahliae Kleb. A rapid and user-friendly early diagnostic technique is essential for the preventing and controlling VW disease. In this st...

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

International journal of biometeorology
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns th...

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton.

Sensors (Basel, Switzerland)
Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT technique...

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

Scientific reports
Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed manag...