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A greedy approach to improve pesticide application for precision agriculture using model predictive control

Pests may lead to low crop productivity and profitability. Pesticides are commonly used to protect crops from pests. However, too much pesticide is not only associated with harmful effects to the environment but may also lead to sub-optimal pest management. The existing works focus on the vehicle ro...

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Main Authors: Zangina, Umar, Buyamin, Salinda, Aman, Muhammad Naveed, Zainal Abidin, Mohamad Shukri, Mahmud, Mohd. Saiful Azimi
Format: Article
Published: Elsevier B.V. 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/96489/
http://dx.doi.org/10.1016/j.compag.2021.105984
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spelling my.utm.964892022-07-24T11:15:26Z http://eprints.utm.my/id/eprint/96489/ A greedy approach to improve pesticide application for precision agriculture using model predictive control Zangina, Umar Buyamin, Salinda Aman, Muhammad Naveed Zainal Abidin, Mohamad Shukri Mahmud, Mohd. Saiful Azimi TK Electrical engineering. Electronics Nuclear engineering Pests may lead to low crop productivity and profitability. Pesticides are commonly used to protect crops from pests. However, too much pesticide is not only associated with harmful effects to the environment but may also lead to sub-optimal pest management. The existing works focus on the vehicle routing problem for pesticide management without giving due consideration to finding the optimal time, amount, and area for pesticide application. To solve this issue, this paper takes an active stance and introduces demand management for pesticide using an active mass-spring suspension system. Moreover, using a controller based on model predictive control that uses the active demand management model, this paper efficiently solves the problem of finding the right time, amount and place for pesticide application in an agricultural field. A greedy algorithm is then proposed to solve the vehicle routing problem after identifying the optimal time, and place for pesticide application. The proposed solution minimizes the risk of pest infestation by considering pest risk prediction models. The simulation results show that the proposed technique can maximize the protection for crops against pests. Moreover, a performance analysis of the proposed technique shows that it has significantly lower computational complexity and can converge to the optimal solution at least 78% faster than existing techniques. Elsevier B.V. 2021-03 Article PeerReviewed Zangina, Umar and Buyamin, Salinda and Aman, Muhammad Naveed and Zainal Abidin, Mohamad Shukri and Mahmud, Mohd. Saiful Azimi (2021) A greedy approach to improve pesticide application for precision agriculture using model predictive control. Computers and Electronics in Agriculture, 182 . p. 105984. ISSN 0168-1699 http://dx.doi.org/10.1016/j.compag.2021.105984 DOI:10.1016/j.compag.2021.105984
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zangina, Umar
Buyamin, Salinda
Aman, Muhammad Naveed
Zainal Abidin, Mohamad Shukri
Mahmud, Mohd. Saiful Azimi
A greedy approach to improve pesticide application for precision agriculture using model predictive control
description Pests may lead to low crop productivity and profitability. Pesticides are commonly used to protect crops from pests. However, too much pesticide is not only associated with harmful effects to the environment but may also lead to sub-optimal pest management. The existing works focus on the vehicle routing problem for pesticide management without giving due consideration to finding the optimal time, amount, and area for pesticide application. To solve this issue, this paper takes an active stance and introduces demand management for pesticide using an active mass-spring suspension system. Moreover, using a controller based on model predictive control that uses the active demand management model, this paper efficiently solves the problem of finding the right time, amount and place for pesticide application in an agricultural field. A greedy algorithm is then proposed to solve the vehicle routing problem after identifying the optimal time, and place for pesticide application. The proposed solution minimizes the risk of pest infestation by considering pest risk prediction models. The simulation results show that the proposed technique can maximize the protection for crops against pests. Moreover, a performance analysis of the proposed technique shows that it has significantly lower computational complexity and can converge to the optimal solution at least 78% faster than existing techniques.
format Article
author Zangina, Umar
Buyamin, Salinda
Aman, Muhammad Naveed
Zainal Abidin, Mohamad Shukri
Mahmud, Mohd. Saiful Azimi
author_facet Zangina, Umar
Buyamin, Salinda
Aman, Muhammad Naveed
Zainal Abidin, Mohamad Shukri
Mahmud, Mohd. Saiful Azimi
author_sort Zangina, Umar
title A greedy approach to improve pesticide application for precision agriculture using model predictive control
title_short A greedy approach to improve pesticide application for precision agriculture using model predictive control
title_full A greedy approach to improve pesticide application for precision agriculture using model predictive control
title_fullStr A greedy approach to improve pesticide application for precision agriculture using model predictive control
title_full_unstemmed A greedy approach to improve pesticide application for precision agriculture using model predictive control
title_sort greedy approach to improve pesticide application for precision agriculture using model predictive control
publisher Elsevier B.V.
publishDate 2021
url http://eprints.utm.my/id/eprint/96489/
http://dx.doi.org/10.1016/j.compag.2021.105984
_version_ 1739828087133044736
score 13.211869

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