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 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.
Researchers introduce a novel approach using TinyML sensors and models to estimate the shelf life of fresh dates non-destructively. The study develops a lightweight TinyML system combining a miniature NIR spectral sensor and an Arduino microcontroller for on-device inference. This edge computing approach enables real-time prediction of date shelf life, eliminating the need for continuous cloud connectivity.
Researchers present an innovative study focused on accurate temperature prediction for greenhouse management. By comparing Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM) models, they identify the RBF model with the Levenberg–Marquardt (LM) learning algorithm as the most effective. This model achieves precise greenhouse temperature forecasting, enhancing crop yields, and minimizing energy waste.
This article introduces cutting-edge deep learning techniques as a solution to combat evolving web-based attacks in the context of Industry 5.0. By merging human expertise and advanced models, the study proposes a comprehensive approach to fortify cybersecurity, ensuring a safer and more resilient future for transformative technologies.
Amid the imperative to enhance crop production, researchers are combating the threat of plant diseases with an innovative deep learning model, GJ-GSO-based DbneAlexNet. Presented in the Journal of Biotechnology, this approach meticulously detects and classifies tomato leaf diseases. Traditional methods of disease identification are fraught with limitations, driving the need for accurate, automated techniques.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.