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Open access

ISSN: 2589-7217
CN: 10-1795/S
p-ISSN: 2097-2113

Unveiling the drivers contributing to global wheat yield shocks through quantile regression

Sudden reductions in crop yield (i.e., yield shocks) severely disrupt the food supply, intensify food insecurity, depress farmers' welfare, and worsen a country's economic conditions. Here, we study...

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A new tool to improve the computation of animal kinetic activity indices in precision poultry farming

Precision Livestock Farming (PLF) emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies. PLF provides farmers with...

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Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach

Predicting grapevine phenological stages (GPHS) is critical for precisely managing vineyard operations, including plant disease treatments, pruning, and harvest. Solutions commonly used to address viticulture...

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End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality

Seeds are essential to the agri-food industry. However, their quality is vulnerable to biotic and abiotic stresses during production and storage, leading to various types of deterioration. Real-time...

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Stereo vision based broccoli recognition and attitude estimation method for field harvesting

At present, automatic broccoli harvest in field still faces some issues. It is difficult to segment broccoli in real time under complex field background, and hard to pick tilt-growing broccoli for the...

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AI-driven aquaculture: A review of technological innovations and their sustainable impacts

The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization....

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PWM offline variable application based on UAV remote sensing 3D prescription map

Precision application in orchards enhancing deposition uniformity and environmental sustainability by accurately matching nozzle output with canopy parameters. This study provides a pipeline for creating...

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Automatic location and recognition of horse freezing brand using rotational YOLOv5 deep learning network

Individual livestock identification is of great importance to precision livestock farming. Liquid nitrogen freezing labeled horse brand is an effective way for livestock individual identification. Along...

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A review of external quality inspection for fruit grading using CNN models

This article reviews the state of the art of recent CNN models used for external quality inspection of fruits, considering parameters such as color, shape, size, and defects, used to categorize fruits...

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Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

Selecting techniques is a crucial aspect of disease detection analysis, particularly in the convergence of computer vision and agricultural technology. Maintaining crop disease detection in a timely...

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Estimating TYLCV resistance level using RGBD sensors in production greenhouse conditions

Automated phenotyping is the task of automatically measuring plant attributes to help farmers and breeders in developing and growing strong robust plants. An automated tool for early illness detection...

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Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull

The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output...

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Utility-based regression and meta-learning techniques for modeling actual ET: Comparison to (METRIC-EEFLUX) model

Estimating actual evapotranspiration (ETₐ) is crucial for water resource management, yet existing methods face limitations. Traditional approaches, including eddy covariance and remote sensing-based...

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Neural network architecture search enabled wide-deep learning (NAS-WD) for spatially heterogenous property awared chicken woody breast classification and hardness regression

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions....

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A salient feature establishment tactic for cassava disease recognition

Accurate classification of cassava disease, particularly in field scenarios, relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic...

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

In this study, we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal....

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An artificial neuronal network coupled with a genetic algorithm to optimise the production of unsaturated fatty acids in Parachlorella kessleri

In this study, an Artificial Neural Network-Genetic Algorithm (ANN-GA) approach was successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids...

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A comprehensive survey on weed and crop classification using machine learning and deep learning

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos. This technology plays a crucial role in facilitating...

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Estimation of flea beetle damage in the field using a multistage deep learning-based solution

Estimation of damage in plants is a key issue for crop protection. Currently, experts in the field manually assess the plots. This is a time-consuming task that can be automated thanks to the latest...

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Computer vision in smart agriculture and precision farming: Techniques and applications

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial...

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Image classification on smart agriculture platforms: Systematic literature review

In recent years, smart agriculture has gained strength due to the application of industry 4.0 technologies in agriculture. As a result, efforts are increasing in proposing artificial vision applications...

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Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments

Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational...

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Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security,...

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UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

Accurate watermelon yield estimation is crucial to the agricultural value chain, as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning. The conventional...

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Deep learning-based intelligent precise aeration strategy for factory recirculating aquaculture systems

Factory recirculating aquaculture system (RAS) is facing in a stage of continuous research and technological innovation. Intelligent aquaculture is an important direction for the future development...

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