Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis.

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics.

Sustainable poultry farming practices: a critical review of current strategies and future prospects.

As global demand for poultry products, environmental sustainability, and health consciousness rises with time, the poultry industry faces both substantial challenges and new opportunities. Therefore, this review paper provides a comprehensive overview of sustainable poultry farming, focusing on integrating genetic improvements, alternative feed, precision technologies, waste management, and biotechnological innovations. Together, these strategies aim to minimize ecological footprints, uphold ethical standards, improve economic feasibility, and enhance industry resilience.

Tracking dustbathing behavior of cage-free laying hens with machine vision technologies.

Dustbathing (DB) is a functionally important maintenance behavior in birds that clean plumage, realigns feather structures, removes feather lipids, which helps to remove parasites and prevents feathers from becoming too oily. Among different natural behaviors birds perform in cage-free (CF) housing, DB is one of the important behavior related to bird welfare. Earlier studies have identified DB behavior using manual method such as counting number of DB bouts, and duration of DB bouts from video recordings.

Tracking perching behavior of cage-free laying hens with deep learning technologies.

Providing perches in cage-free (CF) housing offers significant benefits for laying hens, such as improved leg muscle development, bone health, reduced abdominal fat, and decreased fear and aggression. A precise detection method is essential to ensure that hens engage in perching behavior from an early age, as manual observation is often labor-intensive and sometimes inaccurate. The objectives of this study were to (1) develop and test a deep learning model for detecting perching behavior; and (2) evaluate the optimal model's performance on detecting perching behavior of laying hens of different ages.

Monitoring activity index and behaviors of cage-free hens with advanced deep learning technologies.

Chickens' behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNN) models were developed to monitor the chicken activity index. A dataset consisting of 1,500 top-view images was utilized to construct tracking models, with 900 images allocated for training, 300 for validation, and 300 for testing.

Automatic detection of bumblefoot in cage-free hens using computer vision technologies.

Cage-free (CF) housing systems are expected to be the dominant egg production system in North America and European Union countries by 2030. Within these systems, bumblefoot (a common bacterial infection and chronic inflammatory reaction) is mostly observed in hens reared on litter floors. It causes pain and stress in hens and is detrimental to their welfare.

Deep Learning Methods for Tracking the Locomotion of Individual Chickens.

Poultry locomotion is an important indicator of animal health, welfare, and productivity. Traditional methodologies such as manual observation or the use of wearable devices encounter significant challenges, including potential stress induction and behavioral alteration in animals. This research introduced an innovative approach that employs an enhanced track anything model (TAM) to track chickens in various experimental settings for locomotion analysis.

Electrostatic particle ionization for suppressing air pollutants in cage-free layer facilities.

The increasing demand for cage-free (CF) poultry farming raises concern regarding air pollutant emissions in these housing systems. Previous studies have indicated that air pollutants such as particulate matter (PM) and ammonia (NH) pose substantial risks to the health of birds and workers. This study aimed to evaluate the efficacy of electrostatic particle ionization (EPI) technology with different lengths of ion precipitators in reducing air pollutants and investigate the relationship between PM reduction and electricity consumption.

A Computer Vision-Based Automatic System for Egg Grading and Defect Detection.

Defective eggs diminish the value of laying hen production, particularly in cage-free systems with a higher incidence of floor eggs. To enhance quality, machine vision and image processing have facilitated the development of automated grading and defect detection systems. Additionally, egg measurement systems utilize weight-sorting for optimal market value.

Automatic detection of brown hens in cage-free houses with deep learning methods.

Computer vision technologies have been tested to monitor animals' behaviors and performance. High stocking density and small body size of chickens such as broiler and cage-free layers make effective automated monitoring quite challenging. Therefore, it is critical to improve the accuracy and robustness of laying hens clustering detection.

Mislaying behavior detection in cage-free hens with deep learning technologies.

Floor egg-laying behavior (FELB) is one of the most concerning issues in commercial cage-free (CF) houses because floor eggs (i.e., mislaid eggs on the floor) result in high labor costs and food safety concerns.

Tracking floor eggs with machine vision in cage-free hen houses.

Some of the major restaurants and grocery chains in the United States have pledged to buy cage-free (CF) eggs only by 2025 or 2030. While CF house allows hens to perform more natural behaviors (e.g.

Ammonia emissions, impacts, and mitigation strategies for poultry production: A critical review.

Confined animal feeding operations (CAFOs) are the main sources of air pollutants such as ammonia (NH) and greenhouse gases. Among air pollutants, NH is one of the most concerned gasses in terms of air quality, environmental impacts, and manure nutrient losses. It is recommended that NH concentrations in the poultry house should be controlled below 25 ppm.

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor.

Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition.