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.
This paper delves into the extensive use of artificial intelligence (AI) models for assessing food security indicators across the globe, with a notable focus on sub-Saharan Africa. The study emphasizes the importance of stakeholder involvement in AI modeling for food security, highlighting three key approaches to integrating AI into food security research.
This article discusses the significant roles that robotics play in the food industry. Precision agriculture robots are optimizing crop management, while 3D food printing is revolutionizing personal food preparation. These technological advancements have the potential to enhance food quality, sustainability, and accessibility, all while creating new opportunities for human involvement in food-related jobs.
Tenchijin, a Japanese startup, is utilizing deep learning and satellite data to address issues with satellite internet, particularly the impact of weather on ground stations. Their AI system accurately predicts suitable ground stations, providing more reliable internet connectivity, and their COMPASS service has applications in renewable energy, agriculture, and city planning by optimizing land use decisions using a variety of data sources.
This article discusses the application of machine learning models to predict anomalies in daily maximum temperatures in India from March to June. The study evaluates various machine learning models and identifies an optimal model, emphasizing its effectiveness in forecasting extreme temperature events, with the potential to complement numerical weather prediction models.
This research paper discusses the application of machine learning algorithms to predict the Water Quality Index (WQI) in groundwater in Sakrand, Pakistan. The study collected data samples, applied various classifiers, and found that the linear Support Vector Machine (SVM) model demonstrated the highest prediction accuracy for both raw and normalized data, with potential applications in assessing groundwater quality for various purposes, including drinking and irrigation.
Researchers conducted a comprehensive bibliometric exploration of non-destructive testing techniques for assessing fruit quality. Leveraging Web of Science data, they unveiled evolving research trends, hotspots, and the promising integration of advanced technologies like machine vision and deep learning, offering valuable insights for the fruit industry's competitiveness and quality assurance.
Researchers harnessed artificial intelligence to predict groundwater levels in Ethiopia's Bilate watershed, a water-scarce region. Their study revealed that Gradient Boosting Regression (GBR) performed exceptionally well, offering a valuable tool for sustainable borehole drilling decisions, particularly for irrigation, in water-scarce regions.
The Crop Planting Density Optimization System (CPDOS) harnesses the power of artificial intelligence, including genetic algorithms and neural networks, to optimize crop planting density for improved agricultural yields. This intelligent online system offers advanced tools for farmers to fine-tune planting density and fertilizer application, ultimately enhancing crop production while considering economic factors.
Researchers have leveraged machine learning and deep learning techniques, including BiLSTM networks, to classify maize gene expression profiles under biotic stress conditions. The study's findings not only demonstrate the superior performance of the BiLSTM model but also identify key genes related to plant defense mechanisms, offering valuable insights for genomics research and applications in developing disease-resistant maize varieties.
Researchers have developed a novel method that combines geospatial artificial intelligence (GeoAI) with satellite imagery to predict soil physical properties such as clay, sand, and silt. They utilized a hybrid CNN-RF model and various environmental parameters to achieve accurate predictions, which have significant implications for agriculture, erosion control, and environmental monitoring.
Researchers have introduced an innovative Intrusion Detection System (IDS) model, IDSNet-PDO, built on one-dimensional convolutional neural networks (1D-CNN) and fine-tuned with the Prairie Dog Optimization (PDO) algorithm. This IDS model demonstrates high accuracy in predicting Distributed Denial of Service (DDoS) attacks in the context of Agriculture 4.0, addressing cybersecurity challenges in interconnected IoT devices used in modern agriculture.
This paper introduces YOLOv5n-VCW, an advanced algorithm for tomato pest and disease detection, leveraging Efficient Vision Transformer, CARAFE upsampling, and WIoU Loss to enhance accuracy while reducing model complexity. Experimental results demonstrate its superiority over existing models, making it a promising tool for practical applications in agriculture.
Researchers have introduced an innovative flooding-based MobileNet V3 approach for the accurate and efficient identification of cucumber diseases from farmer-captured leaf images. This lightweight and mobile-friendly solution holds significant promise for improving crop disease detection and aiding farmers in the early diagnosis of cucumber diseases, addressing the limitations of traditional manual inspection methods.
This research highlights the use of AI and open-source tools to address climate change challenges in Côte d'Ivoire's agriculture. It introduces AI models for cocoa plant health monitoring and water resource forecasting, emphasizing their potential in promoting sustainable practices and climate-resilient decision-making for farmers and policymakers.
Researchers harness the power of pseudo-labeling within semi-supervised learning to revolutionize animal identification using computer vision systems. They also explored how this technique leverages unlabeled data to significantly enhance the predictive performance of deep neural networks, offering a breakthrough solution for accurate and efficient animal identification in resource-intensive agricultural environments.
Researchers recently published an article in the journal Drones, showcasing the success of the stepwise soft actor-critic (SeSAC) method in achieving autonomous flight control for unmanned aerial vehicles (UAVs). Addressing limitations in existing control techniques, SeSAC combines reinforcement learning with efficient learning strategies to navigate complex real-world environments.
Researchers explore the power of machine learning models to predict effective microbial strains for combatting drought's impact on crop production. By comparing various models, the study reveals that gradient boosted trees (GBTs) offer high accuracy, though considerations of computational resources and application needs are vital when choosing a model for real-world implementation.
Researchers investigate the application of Graph Neural Networks (GNNs) for forecasting trade values in food and agriculture between nations. The study introduces robust baselines and evaluates static and dynamic GNN models using the United Nations Trade dataset. The findings highlight the strength of baselines and the potential of GNNs, particularly the Temporal Graph Networks (TGN).
Researchers combine deep neural networks (DNN) with a PID-RENet (Proportional-Integral-Derivative Residual Elimination Network) to improve time-series water quality predictions in aquaculture. The PID-RENet approach effectively corrects DNN predictions using PID control principles, leading to more accurate forecasts for crucial water quality parameters.
Researchers devise interpretable and non-interpretable ML models optimized by particle swarm optimization to accurately estimate crop evapotranspiration for winter wheat. By utilizing limited meteorological data, these models offer insights into water usage and agricultural sustainability, aiding water management practices in the face of climate challenges.
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