Neural predictive control of broiler chicken and pig growth

Abstract

Active control of the growth of broiler chickens and pigs has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health in broiler chickens. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent animal growth using a nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predicative control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler and pig growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler and pig weights accurately followed the desired growth curves ranging from −12% to +12% and −20% to +20% of the standard curve for broiler chickens and pigs, respectively. The overall mean relative error between the desired and achieved broiler or pig weight was 1.8% for the period from day 12 to day 51 and 10.5% for the period from week 5 to week 21, respectively.

How Are Information Technologies Addressing Broiler Welfare? A Systematic Review Based on the Welfare Quality® Assessment

“…Most of the ITs assessed were first implemented experimentally as a first step to develop a practical tool for farmers (Appendix A). For example, the development of an algorithm to control the growth of broilers was developed by Demmers et al [81]; however, no studies in the present SR evaluate such an IT in a real broiler house environment.…”

“…They were primarily used to process image data involved with the detection and assessment of broiler lameness and/or leg disorders [23,[36][37][38][39][47][48][49][50][51][52][53]55], to characterize chick behaviour under different temperatures [46] and other environmental conditions [30], to detect equipment malfunctioning [54], and for early detection of sick broilers [69]. In addition, they were used to process sound data and to provide information about feeding and/or drinking behaviours [24,44], to automate the detection of footpad dermatitis along the slaughter line [68], to define the best positions to install CO 2 sensors in a broiler house [75], to control broiler chickens growth curve [81], and to develop an innovative image display tool that allowed farmers to assess broilers' living conditions [28].…”

“…Demmers et al [81] 2018 UK, China Algorithm was used to predict broiler's growth. Broiler farm Mean relative error between desired and achieved broiler weight was 1.8%.…”

Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

“…Recently, machine learning has been actively used in several fields with the development of computer performance. Machine learning in the livestock field is also actively used to analyze animal behavioral pattern [19,20,[26][27][28], to analyze behavior prior to calving [29][30][31], to analyze the voice of livestock [32], and to predict dependent variables according to various environmental variables [18]. Among several machine learning techniques, ANN has been actively used as a method to accurately predict the dependent variables from independent variables.…”

“…Recently, artificial neural networks (ANNs) have been actively used because of their ability to accurately predict the dependent variables from independent variables [16,17]. The recurrent neural network (RNN) model, which is a type of ANN model, has been actively applied to the agricultural field due to the advantage of being suitable for dealing with time-series data [18][19][20][21][22]. Several studies have also used ANN models to analyze and predict the weather data [23,24].…”