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 propose an AI-powered posture classification system, employing MoveNet and machine learning, to address ergonomic challenges faced by agricultural workers. The study demonstrates the feasibility of leveraging AI for precise posture detection, offering potential advancements in safety practices and worker health within the demanding agricultural sector.
Researchers propose an AI-powered robotic crop farm, Agrorobotix, utilizing deep reinforcement learning (DRL) for enhanced urban agriculture. Tested in simulated conditions, Agrorobotix showcased a 16.3% increase in crop yield, 21.7% reduced water usage, and a 33% decline in chemical usage compared to conventional methods, highlighting its potential to transform urban farming, improve food security, and contribute to smart city development.
Researchers proposed a hybrid optimization approach, combining Artificial Neural Network (ANN) and Genetic Algorithm (GA), to enhance plastic injection molding. Addressing quality, production efficiency, and sustainability, the method demonstrated effectiveness in achieving global multi-objective optimization, providing a valuable tool for smart, sustainable, and economically efficient production processes.
This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.
Researchers present a novel microclimate model for precision agriculture in Bergamo, Italy, blending neural networks and physical modeling. Assessing the impact of global (ERA5) versus local (ARPA) climate data, the model achieved high accuracy in temperature predictions, emphasizing the role of neural networks in capturing intricate variations. The study contributes valuable insights for optimizing input data in microclimate modeling, vital for informed decision-making in precision agriculture.
This article introduces a novel machine learning approach for non-invasive broiler weight estimation in large-scale production. Utilizing Gaussian mixture models, Isolation Forest, and OPTICS algorithm in a two-stage clustering process, the researchers achieved accurate predictions of individual broiler weights. The comprehensive methodology, combining polynomial fitting, gray models, and adaptive forecasting, offers a promising and cost-effective solution for precise broiler weight monitoring in large-scale farming setups, as evidenced by considerable accuracy in evaluations across 111 datasets.
Researchers introduce a pioneering approach to swarm robotics, leveraging blockchain technology for decentralized economic incentives. The study demonstrates the feasibility and advantages of a blockchain-based information marketplace, fostering cooperation and penalizing misinformation among robots with conflicting interests in open swarms. This innovation holds promise for real-world applications beyond robotics, ushering in a new era of decentralized, trustless networks inspired by the intersection of swarm robotics and blockchain technology.
This study presents an innovative method for predicting individual Chinese cabbage harvest weight using unmanned aerial vehicles (UAVs) and multi-temporal features. By automating plant detection with an object detection algorithm and leveraging various UAV data sources, the study achieves accurate and early predictions, addressing limitations in existing methods and offering valuable insights for precision agriculture and crop management.
This study introduces an innovative framework for early plant disease diagnosis, leveraging fog computing, IoT sensor technology, and a novel GWO algorithm. The hybrid approach, incorporating deep learning models like AlexNet and GoogleNet, coupled with modified GWO for feature selection, demonstrates superior performance in plant disease identification.
Researchers explored the application of artificial intelligence (AI), specifically long short-term memory (LSTM) and artificial neural networks (ANN), in assessing and predicting surface water quality. The study, conducted on the Ashwini River in Himachal Pradesh, India, showcased the effectiveness of LSTM models in accurate water quality prediction, emphasizing the potential of AI in resource management and environmental protection
This study introduces a novel approach for forecasting sugarcane yield in major Chinese production regions. Utilizing the Water Cycle Algorithm (WCA) to fine-tune the Least Squares Support Vector Machine (LSSVM) model, the proposed method demonstrates superior accuracy and generalization capabilities, offering valuable insights for optimizing sugarcane production practices.
This review article discusses the evolution of machine learning applications in weather and climate forecasting. It outlines the historical transition from statistical methods to physical models and the recent emergence of machine learning techniques. The article categorizes machine learning applications in climate prediction, covering both short-term weather forecasts and medium-to-long-term climate predictions.
This study explores the application of deep learning models to segment sheep Loin Computed Tomography (CT) images, a challenging task due to the lack of clear boundaries between internal tissues. The research evaluates six deep learning models and identifies Attention-UNet as the top performer, offering exceptional accuracy and potential for improving livestock breeding and phenotypic trait measurement in living sheep.
This paper explores the integration of IoT with drone technology to enhance data communication and security across various industries, including agriculture and smart cities. The study focuses on the use of machine learning and deep learning techniques to detect cyberattacks within drone networks and presents a comprehensive framework for intrusion detection.
This article outlines the development and implementation of a smart greenhouse system using IoT technologies. This comprehensive solution integrates automation, precise growth parameter fine-tuning, and user-friendly Android mobile interfaces, offering real-time control and improved crop management in Brassica Juncea cultivation.
This study, published in Nature, explores the application of Convolutional Neural Networks (CNN) to identify and detect diseases in cauliflower crops. By using advanced deep-learning models and extensive image datasets, the research achieved high accuracy in disease classification, offering the potential to enhance agricultural efficiency and ensure food security.
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.
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