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 Tsinghua University have developed FROM-GLC Plus 3.0, an AI-integrated land monitoring framework that combines satellites, near-surface cameras, and segmentation models for real-time land cover mapping. This multimodal system overcomes cloud interference and missing transitions, achieving daily accuracy and parcel-level insights crucial for agriculture, conservation, and climate resilience.
Researchers at Guizhou University have developed LLRL, a deep learning framework that targets lesion regions directly, boosting the accuracy of plant disease severity assessment across apple, potato, and tomato leaves. The model achieved up to 92.4% accuracy, outperforming 12 benchmark approaches.
Researchers at Stanford Medicine have developed CRISPR-GPT, an Artificial intelligence copilot that guides scientists through designing and troubleshooting CRISPR gene-editing experiments. Trained on 11 years of expert data, it accelerates experiment planning, predicts off-target effects, and lowers the barrier for newcomers to gene editing.
This study developed a transformer-based AI model using UAV multispectral and RGB imaging to predict wheat yields with unprecedented accuracy. The approach allows non-destructive, cost-effective, and early identification of high-yield lines, speeding up breeding for climate-resilient wheat.
University of Houston researchers are developing an AI-powered dashboard to help food pantries in Florida coordinate disaster relief and reach food-insecure families more efficiently. The $300,000 USDA-funded project builds on lessons from Hurricane Harvey and aims to expand nationwide.
Hexion has unveiled SmartQuality, a cutting-edge AI-powered system designed to optimize wood panel manufacturing by providing real-time quality insights and enhancing operational efficiency. Launched at LIGNA 2025, SmartQuality aims to resolve the trade-off between line speed and product quality, supporting sustainable and data-driven production.
A new study presents an advanced remote sensing and machine learning model that accurately distinguishes between natural and human-driven water consumption in arid croplands. Findings from the Ebinur Lake Basin show agriculture now drives 77% of cropland water use, with profound implications for regional water security.
Researchers from the University of Illinois developed an AI-driven approach combining stacking ensemble machine learning models and SHapley Additive exPlanations (SHAP) to predict gully erosion susceptibility with high accuracy. Their methodology enhances both the precision and interpretability of erosion risk assessments, guiding more effective conservation efforts.
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.
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