AI in agriculture leverages technologies like machine learning, computer vision, and data analytics to optimize farming practices, crop management, and resource utilization. It enables tasks such as automated monitoring, disease detection, yield prediction, and precision farming, leading to increased efficiency, improved productivity, and sustainable agriculture practices.
Researchers at the University of Delaware have developed an AI-powered model that predicts the risk of musculoskeletal injuries in athletes after a concussion with 95% accuracy. By analyzing over 100 personalized factors, the tool offers a major step forward in injury prevention and post-concussion care.
Researchers have developed an AI-powered monitoring system that links individual honeybee exposure to neonicotinoid pesticides with colony-wide health effects. Even low-level exposure was shown to reduce pollen foraging efficiency, impacting the entire hive.
Researchers from NOVA IMS have developed Counterfactual SMOTE, an advanced oversampling method that improves minority class detection in imbalanced healthcare datasets. By generating boundary-focused, noise-free synthetic samples, it significantly enhances AI model accuracy, especially for rare but critical outcomes.
Researchers at Tulane University have developed an AI-powered Group Association Model that accurately detects antibiotic resistance in tuberculosis and staph by analyzing whole genome sequences—outperforming current WHO methods and reducing false positives.
Researchers at Iowa State University are creating high-resolution 3D digital twins of plants using AI-driven neural radiance fields (NeRF), transforming simple smartphone videos into dynamic, data-rich models. These digital twins are advancing agriculture, healthcare, and manufacturing by enabling real-time simulations, precision predictions, and enhanced decision-making.
AI researchers in Japan have developed ‘Plant Doctor,’ an automated system that uses video footage and hybrid AI to accurately assess the health of urban plants without harming them. This scalable, non-invasive tool could revolutionize both urban forestry and agriculture.
Researchers from KIIT and Chandragupt Institute of Management explore how machine learning transforms big data challenges into opportunities, enabling industries to harness vast data resources effectively.
Researchers developed a machine learning model to map animal feeding operations with 87% accuracy, addressing data gaps crucial for environmental management.
Loughborough University researchers have developed AI tools to help reduce greenhouse gas emissions from UK livestock farming and land use, supporting the 2050 net zero goal.
Korean researchers showcased cutting-edge startup technologies at CES 2025, reinforcing South Korea’s global presence in innovation and entrepreneurship. Five ETRI-born companies won CES Innovation Awards, highlighting the success of ETRI’s technology incubator.
Scientists at the Weizmann Institute developed an AI-driven pipeline to predict molecular targets of natural toxins, using a cone snail toxin as a model. This breakthrough enhances ecological research and drug development by identifying precise protein interactions.
Researchers have developed EasyDAM_V4, an AI-driven fruit detection model that enhances labeling accuracy using a Guided-GAN approach, improving automation in agricultural image processing.
AI job postings in the U.S. surged by 68% from Q4 2022 to Q4 2024, despite an overall 17% decline in job postings. A new study from the University of Maryland highlights the "ChatGPT effect" driving this trend, with AI roles expanding across multiple economic sectors.
Generative AI accelerates the discovery of safe, scalable solutions to reduce methane emissions in cattle, aiding climate change mitigation goals.
Researchers used a vast, multilingual dataset to systematically review how artificial intelligence is applied in global climate research, identifying China as the leading contributor and uncovering opportunities for AI to further impact climate science.
Researchers from Xinjiang University have developed an improved ant colony algorithm for dynamic job allocation in agricultural machinery, reducing operational costs and improving efficiency in smart agriculture.
Researchers explore green AI as a key approach to minimizing AI's environmental impact through energy-efficient algorithms and hardware, driving sustainability without sacrificing performance.
AI reduces energy consumption by up to 32.34% in plant factories, optimizing resource efficiency for sustainable food production across diverse climates.
This research reviews 876 articles on water prediction, showcasing the evolution of ML and DL techniques and highlighting significant contributors and trends.
Using advanced machine learning algorithms, researchers successfully classified soils based on their parent materials, achieving up to 100% accuracy. The study highlights the potential of ML techniques like ESKNN and SVM in precise soil source determination across various analytical methods.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.