Modern agriculture is a data-rich but decision-constrained domain, where traditional methods struggle to keep pace with ...
Dr. Vishal Sharma [email protected] Not long ago, the word “Artificial Intelligence (AI)” would bring to mind ...
Desertification threatens 24% of the world's land area spanning 126 countries and impacts 35% of the global population. Yet ...
Overview AI systems use sensors and computer vision to detect pests and diseases early, reducing crop damage and yield losses ...
A research team has introduced a lightweight artificial intelligence method that accurately identifies wheat growth stages ...
By transferring temporal knowledge from complex time-series models to a compact model through knowledge distillation and attention mechanisms, the ...
A research team has developed a new model, PlantIF, that addresses one of the most pressing challenges in agriculture: the ...
Plant phenotypes—observable traits shaped by genetics and environment—are the foundation of breeding and crop science. Traditional trait measurement relies ...
1 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia 2 InnoV'COM Laboratory-Sup'Com, University of Carthage, Ariana, Tunisia ...
Introduction: The rapid growth of the global population and intensive agricultural activities has posed serious environmental challenges. In response, there is an increasing demand for sustainable ...
Abstract: The study presents a novel use of Convolutional Neural Network (CNN) algorithms for crop prediction and soil type classification. Soil type classification is crucial for agricultural ...
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