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

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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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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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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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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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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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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Classifying early apple scab infections in multispectral imagery using convolutional neural networks

Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards....

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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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Cross-comparative review of Machine learning for plant disease detection: apple, cassava, cotton and potato plants

Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agriculture and food security. Detecting these diseases early can help mitigate their impact...

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Towards sustainable agriculture: Harnessing AI for global food security

The issue of food security continues to be a prominent global concern, affecting a significant number of individuals who experience the adverse effects of hunger and malnutrition. The finding of a solution...

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Hazelnut mapping detection system using optical and radar remote sensing: Benchmarking machine learning algorithms

Mapping hazelnut orchards can facilitate land planning and utilization policies, supporting the development of cooperative precision farming systems. The present work faces the detection of hazelnut...

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LeafSpotNet: A deep learning framework for detecting leaf spot disease in jasmine plants

Leaf blight spot disease, caused by bacteria and fungi, poses a threat to plant health, leading to leaf discoloration and diminished agricultural yield. In response, we present a MobileNetV3 based classifier...

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Hyperparameter optimization of YOLOv8 for smoke and wildfire detection: Implications for agricultural and environmental safety

In this study, we extensively evaluated the viability of the state-of-the-art YOLOv8 architecture for object detection tasks, specifically tailored for smoke and wildfire identification with a focus...

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A novel approach based on a modified mask R-CNN for the weight prediction of live pigs

Since determining the weight of pigs during large-scale breeding and production is challenging, using non-contact estimation methods is vital. This study proposed a novel pig weight prediction method...

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Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in the literature concerning...

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Grow-light smart monitoring system leveraging lightweight deep learning for plant disease classification

This work focuses on a novel lightweight machine learning approach to the task of plant disease classification, posing as a core component of a larger grow-light smart monitoring system. To the extent...

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