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 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.
A review in Artificial Intelligence in Agriculture compared machine learning (ML) and deep learning (DL) for weed detection. The study found DL offers higher accuracy, while ML excels in real-time processing with smaller models, addressing challenges like visual similarity and early-stage weed control.
Researchers have developed an advanced machine learning model utilizing long short-term memory (LSTM) to improve the accuracy of predicting extreme rainfall events in Rwanda. This model offers significant insights for climate adaptation and disaster management, especially amid escalating severe weather conditions.
Researchers applied deep learning (DL) models, including ResNet-34, to segment canola plants from other species in the field, treating non-canola plants as weeds. Using datasets containing 3799 canola images, the study demonstrated that ResNet-34 achieved superior performance, highlighting its potential for precision agriculture and innovative weed control strategies.
Researchers compared traditional feature-based computer vision methods with CNN-based deep learning for weed classification in precision farming, emphasizing the former's effectiveness with smaller datasets
Researchers in a recent Smart Agricultural Technology study demonstrated how integrating machine learning (ML) and AI vision into all-terrain vehicles (ATVs) revolutionizes precision agriculture. These technologies automate tasks such as planting and harvesting, enhancing decision-making, crop yield, and operational efficiency while addressing data privacy and scalability challenges.
Researchers developed an automated system utilizing UAVs and deep learning to monitor and maintain remote gravel runways in Northern Canada. This system accurately detects defects and evaluates runway smoothness, proving more effective and reliable than traditional manual methods in harsh and isolated environments.
In their Agronomy journal article, researchers developed a method using RGB-D images and the YOLO-banana neural network to non-destructively localize and estimate the weight of banana bunches in commercial orchards.
Integrating blockchain with the Internet of Drones (IoD) promises enhanced security, connectivity, and efficiency in drone applications like delivery, surveillance, and rescue operations.
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