Deep learning-based decision support system for weeds detection in wheat fields
In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not ef...
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| Published in: | International journal of electrical and computer engineering (Malacca, Malacca) Vol. 12; no. 1; p. 816 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Yogyakarta
IAES Institute of Advanced Engineering and Science
01.02.2022
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| Subjects: | |
| ISSN: | 2088-8708, 2722-2578, 2088-8708 |
| Online Access: | Get full text |
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| Summary: | In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2088-8708 2722-2578 2088-8708 |
| DOI: | 10.11591/ijece.v12i1.pp816-825 |