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 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.
A hybrid quantum deep learning model was developed for rice yield forecasting, combining quantum computing with BiLSTM and XGBoost techniques. This model significantly improved prediction accuracy, supporting global agricultural planning and food security efforts.
Researchers developed TeaPoseNet, a deep neural network for estimating tea leaf poses, focusing on the Yinghong No.9 variety. Trained on a custom dataset, TeaPoseNet improved pose recognition accuracy by 16.33% using a novel algorithm, enhancing tea leaf analysis.
Researchers combined hyperspectral imagery with machine learning models to detect early Fusarium wilt in strawberries. The ANN model achieved the highest accuracy, predicting stress indicators like stomatal conductance and photosynthesis before visual symptoms, enhancing early disease detection and management.
Machine learning models predicted potato leaf blight with 98.3% accuracy using over 4000 weather records. Techniques like K-means clustering, PCA, and copula analysis identified key weather factors. Feature selection significantly enhanced model precision, aiding proactive disease management in agriculture.
Researchers introduced RMS-DETR, a multi-scale feature enhanced detection transformer, to identify weeds in rice fields using UAV imagery. This innovative approach, designed to detect small, occluded, and densely distributed weeds, outperforms existing methods, offering precision agriculture solutions for better weed management and optimized rice production.
Researchers developed a deep learning (DL) approach for non-destructive crop moisture assessment using thermal imagery, focusing on five DL models. Among them, MobilenetV3 excelled in accuracy and speed, demonstrating the potential for real-time water stress monitoring in cotton agriculture, enhancing precision irrigation strategies.
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