Design and Development of Reliable and Automated Hydroponics System | Smart Farming

Geethamani R. (u. a.)

ISBN 10: 6207449576 / ISBN 13: 9786207449576

Published by LAP LAMBERT Academic Publishing, 2023

About this Item

Design and Development of Reliable and Automated Hydroponics System | Smart Farming | Geethamani R. (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786207449576 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 128207062

Bibliographic Details

• Title: Design and Development of Reliable and Automated Hydroponics System | Smart Farming
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 6207449576
• ISBN 13: 9786207449576
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 107 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of the response time. This thesis proposes a detection algorithm that utilizes NN in predicting sensor drifts from time-series data streams. The detection algorithm was later named; CNN, and NB, were designed to predict sensor drifts using forecasting and classification techniques. The algorithms were compared against each other in terms of relevant accuracy metrics for forecasting and classification. The operability of the solution was investigated by developing a web server that hosted the algorithm on an Thing Speak computing instance.