ORCID Profile
0000-0002-4278-3651
Current Organisation
Universiti Putra Malaysia
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IOP Publishing
Date: 11-2019
DOI: 10.1088/1755-1315/355/1/012066
Abstract: Remote sensing is a tool to gather the information about an object or any phenomenon without direct contact or damaging the objects. This technology had numerous application and one of it is in agriculture. Unlike tradition agriculture practiced that difficult to execute and required a large number of man power, implementing this technology will increase the production yield of the crops and improved the agriculture sector in managing and controlling. Remote sensing were able to forecast the crop production, identified the crop type, assess the crop damage and monitoring its progress. Therefore, this research was conducted in order to monitor the early stage of growth of rice crop planted by the farmers in the paddy field using remote sensing. To do so, popular empirical vegetation index known as Normalized Difference Vegetation Index (NDVI) generated from unmanned aerial vehicle (UAV) was selected to monitor the changes of rice crop starting from the day it been planted until eleventh day of planted. Early stage of monitoring the crop growth using NDVI is a best approach to practice. Any damages that occur during this stage will affect the yield production and economy. Result from image analysis shown that NDVI were able to observe the rice crop growth and able to locate the damage part in the paddy plot. Fast action can be made by the farmers to counter attack the damage and treat the problematic points.
Publisher: MDPI AG
Date: 12-11-2021
DOI: 10.3390/APP112210701
Abstract: This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.
Publisher: Universiti Putra Malaysia
Date: 30-04-2021
Abstract: In the current practices, farmers typically rely on the traditional method paper-based for farming data records, which leads to human error. However, the paper-based system can be improved by the mobile app technology to ease the farmers acquiring farm data as all of the farm information will be stored in digital form. This study aimed to develop a smartphone agricultural management app known as Padi2U and implement User Acceptance Test (UAT) for end-users. Padi2U was developed using Master App Builder software and integration with the multispectral imagery. Padi2U provides recommendations based on the Department of Agriculture’s (DOA), such as rice check, pest and disease control, and weed management. Through the Padi2U, farmers can access the field data to understand the crop health status online using the Normalised Difference Vegetation Index (NDVI) map derived from the multispectral images. The NDVI is correlated to the Soil Plant Analysis Development (SPAD) value, corresponding to R² = 0.4012. UAT results showed a 100 percent satisfaction score with suggestions were given to enhance the Padi2U performance. It shows that Padi2U can be improved to help farmers in the field monitoring virtually by integrating multispectral imagery and information from the field.
Publisher: HH Publisher
Date: 30-09-2020
Abstract: This study is focused on paddy growth monitoring using Geographic Information System (GIS) and multispectral imagery via unmanned aerial vehicle (UAV). The objective of the study is to identify the best treatment that produces the highest yield. This combined technology is an effective farming management known as precision farming. UAV was used as a tool for field data capturing to produce orthophoto which will be a source for vegetative index and also for vector data digitizing purposes using ArcGIS 10.2. Data will be used as a source to analyze and monitor paddy growth. Geographical features that are digitized will able to provide farmer a full visual of their crop area such as crop layout, treatment type and also vegetative index. As a result, plot with treatment type Compost with Inoculum is able to produce the highest yield with 2494.7287 t/ha yield comparing to other treatment plots. However, treatment type U Grow producing the highest NDVI reading which is 0.4327 with yield producing only 2411.3080 t/ha lower than the plot with treatment type Compost with Inoculum. Maximum value of NDVI is not a guarantee of highest yield production. However, this research has shown that vegetative index value is able to become a benchmark for paddy growth monitoring while spatial analysis is able to make farming management more efficient. Other factors such as terrain model and effectiveness of current irrigation system can be a next sub topic for the research.
No related grants have been discovered for RHUSHALSHAFIRA ROSLE.