IoT and Deep Learning for Livestock Management

Introduction

Livestock management in agriculture is a complex system that is affected significantly by several factors like regional weather conditions, livestock conditions, market share of livestock farming, and so on. The shed managers or farmers struggle to utilize available resources optimally to attain a good quality product with increased production. It is difficult for a farmer to keep track of the health and well-being of the cattle due to limited manpower and herds of cattle. Livestock production comprises various segments like cattle for milk, meat, eggs, wool, etc. It is observed by the agriculturists that the agriculture sector has major contributors as livestock around the globe. The shed owners or farmers should take care of the livestock owned. The prevalent traditional approaches are insufficient in achieving this goal with pace as desired. There are various factors like monitoring movement of livestock, eating behavior, continuous health monitoring, and the needs of medicines, etc. are major to be considered by the shed owners. It solely depended on the experience of the shed owners.

This chapter is focused to provide an efficient solution to the farmers. Nowadays the technical revolutions, the availability of various sophisticated sensors or wearable gadgets have witnessed the use of technology in various areas or industrial segments. Presently the companies are swiftly adopting the automated monitoring systems to achieve operational efficiency. Such automated systems are potentially helpful in devising effective methods to monitor and classify the various components of the companies. Internet of Things (IoT) and Deep Learning (DL) have been used to obtain these automated monitoring and responding system in different industrial segments. The use of IoT and artificial intelligence-based techniques like deep learning has also been witnessed in the field of cattle monitoring and farming. The automated method of tracking and recognizing the livestock to manage has been efficient ever than the existing manual method. The chapter is targeted to describe the importance of IoT and Deep Learning in Livestock Management through a proposed architecture organized in a layered format, where each layer is performing a specific activity to achieve effective livestock management – the primary motive of the author(s).

The proposed chapter will be divided into different sections to deal with i) problem definition ii) how IoT setup is helpful to handle the problem, iii) data collection and its analysis in real-time through well-drafted deep learning-based algorithms, iv) helping the livestock manager or shed keeper to decide the livestock feed and any medical administration if required. The chapter will start with section (i) that will provide a brief detail on the concept of Internet of Things (IoT), Deep Learning, and how both help dealing with the data heterogeneity to achieve insightful data analytics. With the inclusion of a concrete literature review, the problem will be defined based on the requirement analysis performed. Further, the achievable objectives, to solve the defined problem from the livestock management segment, will be defined. Section (ii) of the chapter will thoroughly discuss the proposed architecture to solve the problems at the side of shed owners or farmers, analytics and distribution of results at the other end. The heuristic-based algorithms using deep learning will be designed to analyze the logged or sensed data and the same will be implemented to handle real-time data for analysis and insightful decision making in the livestock field. The conclusion section will shed light on how the proposed architecture may be used to handle real-time problems faced during livestock management with careful allocation of resources. It will further explain the usefulness of the proposed system in a segment like livestock management, but later o may be scalable to be applied as an agricultural instrument for smart agriculture. Various untouched or yet to be explored areas will also be highlighted for the future scope of research work. At last, the chapter will include the most relevant references including the research work carried out not more than a decade.

The chapter is contributing through the achievement of selected objectives listed below:

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  Justifying the use of IoT and Deep Learning as an effective methodology in dealing with real-time problems

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  Proposing a multi-tier framework for livestock management using IoT and Deep Learning

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  Designing algorithm for data analysis and recommendations to the shed owner or farmer

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  Indicating the potential benefits of the proposed framework

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  Suggesting future research scope in the same field or related