Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Abstract

Remote-monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 years. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; feed intake and greenhouse gas emissions to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and co-variances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link“ to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Opportunities for precision livestock management in the face of climate change: a focus on extensive systems

“…The greatest value from PLM technologies will be realized when combinations of data streams across a property and supply chain are leveraged to inform decision-making. Research is yet to generate models that could be incorporated into commercial platforms, but increasingly, authors are noting their potential ( Tedeschi et al, 2021 ). Through analysis and predictive modeling, integrated PLM data streams could provide producers with an information-dense online interface that offers real-time data pertinent to maintaining a high-efficiency enterprise, thus reducing GHG emissions.…”

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

“…The automated tracking systems can detect and predict behaviors that harm animals such as cannibalism and feather pecking; measuring feed consumption; enhancing production and welfare; light-based movement activity; and quantifying in separate areas to understand preferences of the birds within the pens [13,14]. Due to the surge in the sensor-enabled technologies, now it is feasible to collect video and other physiological data more often consistently on an individual animal basis [15]. This is important because not all farm animal species can be measured the same way.…”

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

“…Although AI has been frequently assigned to the most recent group of technological tools for data analytics (i.e., learning and policy making) to increase operational efficiency, the implementation of AI can be challenging and costly. A learning period is necessary to get the most impactful and powerful benefits of AI, i.e., the monotonous, tedious, and time-consuming tasks requiring the processing of large amounts of data collected through automation and sensor technology ( Tedeschi et al, 2021 ). Given its effective and efficient attributes to find patterns in larger data sets, AI (either machine learning, ML ; or deep learning, DL ) can process data to assist in finding trends to forecast outcomes, but current AI algorithms cannot still explain why and how a result was reached, i.e., AI by itself cannot provide insightful knowledge that leads to wisdom ( Tedeschi, 2019 ).…”

“…The expectations for DSS to solve all problems within a production context were too high, in part because data had limited availability, computational processes were still rustic to the desired outcome, and there was a lack of proper training of the workforce, more specifically the next generation of students that could have made the difference between success and failures in using this technology. Given the increased availability of data through precision livestock farming initiatives ( Tedeschi et al, 2021 ), improved data visualization ( Morota et al, 2021 ), and AI ( Wang et al, 2021 ), the expectations have been renewed with data analytics sparking new motivations to develop more powerful DSS by combining different tools to understand (and apply) the data.…”

“…The fundamental knowledge has not changed; it will forever be. What is changing is the emergence of new analytical techniques that allow us to perform faster data analysis using more extensive, dynamic databases brought about different ways to collect data, better and faster ways to store digital data, and faster computers and algorithms to analyze the data ( Tedeschi, 2019 ; Tedeschi et al, 2021 ). There is nothing wrong with “old statistical methods”; digital devices and their ever-increasing processing speed have facilitated data collection, storage, compilation, and analysis.…”