Agricultural Informatics. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Серия:
Жанр произведения: Программы
Год издания: 0
isbn: 9781119769217
Скачать книгу
in India’s agriculture system that uses the hybrid optimization technique is discussed in Chapter 9. A new hybrid algorithm, i.e., GA-MWPSO, has been used for solving nonlinear constrained optimization problems. To test the competence of the proposed algorithms, a set of test problems has been considered, solved and compared with the existing literature.

      Chapter 10 discusses multicopter unmanned aerial vehicles (UAVs) designed for heavy lift agricultural operations. The knowledge of flying multicopter UAVs and the workings of other components should be strong while developing UAVs; otherwise, the design and assembly of UAVs leads to poor performance or even design fails. Configuration of UAVs includes the size and shape of the UAV and the proper matching of brushless DC electric (BLDC) motors and propellers. Therefore, it is essential to have a deep knowledge about each and every component and its design and selection requirements. In this chapter, information about several UAV systems and components and basic principles of design are presented.

      Chapter 11 describes various security challenges in IoT-enabled agricultural system applications. The challenges facing these systems are software simplicity, secure data generation and transmission, and lack of supporting infrastructure. But at present the biggest obstacle is lack of smooth integration with the agricultural industry and lack of an optimally skilled human workforce. In addition to the need for sensors to work wirelessly and consume low power, they should have better connectivity and remote management, and the complexity and security of software should be rectified. There is also a high demand for fail-safe systems to mitigate the risk of data loss in any faults occurring during operation.

      In summary, at present there is a genuine need for agriculture upgradation and this book provides a technological overview that will open new dimensions which may be useful in discovering solutions to aid in the current growth in agricultural processes. The editors of this book are thankful to the all authors whose valuable contributions made this book as complete.

      Editors Amitava Choudhury University of Petroleum and Energy Studies, Dehradun, India Arindam Biswas Kazi Nazrul University, Asansol, India Manish Prateek University of Petroleum and Energy Studies, Dehradun, India Amlan Chakrabarti University of Calcutta, Kolkata, India January 2021

      1

      A Study on Various Machine Learning Algorithms and Their Role in Agriculture

       Kalpana Rangra and Amitava Choudhury*

      School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

       Abstract

      The term machine learning indicates empowering the machine to gain knowledge and process it for decision making. The domain of crop production is very important for organizations, firms, products related to agriculture. Data collection is done from different sources for crop forecasting. The collected data may vary in shape, size and type depending upon the source of collection. Agricultural data may be collected from metrological sources, agricultural and metrological, soil, sensors that are remotely installed, agricultural statistics, etc. Marketing, storage, transportation and decisions pertaining to crops have high requirement of accurate data that should be produced timely and can be used for predictions.

      Keywords: Agriculture, machine learning, smart farming, decision tree, crop prediction, automated farming, ML models for agriculture

       1.1.1 Machine Learning Model

       1.1.1.1 Artificial Neural Networks