Agricultural robotics research applicable to poultry production: A review Abstract   The advent of agricultural robotics research worldwide has brought substantial improvement for various applications. This article provides a comprehensive review of published research and development work, emphasizing robotics enabling machine capabilities. These machine capabilities of perception, reasoning and learning, communication, task planning and execution, and systems integration have opened possibilities for intelligent automation of current and future agricultural operations, including precision livestock farming. We have focused on the Agricultural Intelligent Automation Systems which have a high potential to be applied to agricultural production and processing, especially with applicability to poultry production. Most of the published work on agricultural robotics has been in the areas of perception and reasoning. The emphases have been in the identification of objects, evaluation of product quality, monitoring of plant and animal growth and development, yield prediction, and machine guidance. There has been limited published work on the task execution and systems integration aspects of agricultural robotics. Moreover, we have reviewed agricultural robotics research from 24 universities worldwide. Agricultural robots can be divided into three categories (monitor, harvester, and both) according to various functions. Several tables are presented to summarize the information on the key subject areas reviewed in this article. We have found that there are still many challenges that need to be addressed in robotizing agricultural tasks in general and in poultry production specifically. The most common challenges in robotics applications have been developing robots for specific agricultural tasks. Examples in poultry production include monitoring environmental conditions and chicken health, egg picking, and encouraging chicken movement. The approaches to addressing the technical needs have been creating intelligent movable machines for use alongside the chickens in poultry house. The most noticeable results include Octopus Poultry Safe (OPS) robot for sanitizing poultry houses autonomously, PoultryBot for picking floor eggs, and Spoutnic for training hens to move. This trend of research and development is expected to continue. An emerging research emphasis is systems approach to study the interactions of automated tasks to achieve high efficiency in whole poultry house management.

Evaluating Convolutional Neural Networks for CageFree Floor Egg Detection The manual collection of eggs laid on the floor (or xe2x80x98floor eggsxe2x80x99) in cagefree (CF) laying hen housing is strenuous and timeconsuming. Using robots for automatic floor egg collection offers a novel solution to reduce labor yet relies on robust egg detection systems. This study sought to develop visionbased flooregg detectors using three Convolutional Neural Networks (CNNs), i.e., single shot detector (SSD), faster regionbased CNN (faster RCNN), and regionbased fully convolutional network (RFCN), and evaluate their performance on floor egg detection under simulated CF environments. The results show that the SSD detector had the highest precision (99.9 xc2xb1 0.1%) and fastest processing speed (125.1 xc2xb1 2.7 msxc2xb7imagexe2x88x921) but the lowest recall (72.1 xc2xb1 7.2%) and accuracy (72.0 xc2xb1 7.2%) among the three flooregg detectors. The RFCN detector had the slowest processing speed (243.2 xc2xb1 1.0 msxc2xb7imagexe2x88x921) and the lowest precision (93.3 xc2xb1 2.4%). The faster RCNN detector had the best performance in floor egg detection with the highest recall (98.4 xc2xb1 0.4%) and accuracy (98.1 xc2xb1 0.3%), and a medium prevision (99.7 xc2xb1 0.2%) and image processing speed (201.5 xc2xb1 2.3 msxc2xb7imagexe2x88x921); thus, the faster RCNN detector was selected as the optimal model. The faster RCNN detector performed almost perfectly for floor egg detection under a wide range of simulated CF environments and system settings, except for brown egg detection at 1 lux light intensity. When tested under random settings, the faster RCNN detector had 91.9xe2x80x9394.7% precision, 99.8xe2x80x93100.0% recall, and 91.9xe2x80x9394.5% accuracy for floor egg detection. It is concluded that a properlytrained CNN flooregg detector may accurately detect floor eggs under CF housing environments and has the potential to serve as a crucial visionbased component for robotic floor egg collection systems.