ORCID Profile
0000-0002-4263-3514
Current Organisation
Universiti Putra Malaysia
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Publisher: Copernicus GmbH
Date: 10-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-4-W16-215-2019
Abstract: Abstract. Affordable housing was developed in order to give equal opportunity for middle and low-incomers in owning a house, especially in Malaysia. To make sure that these people can have a quality house, the National Housing Policy (DRN) with Pelan Tindakan DRN has been introduced by the Malaysian Government to not only provide adequate housing, but also a comfortable, fun and affordable for the wellbeing of the people in Malaysia (KPKT, 2011). Therefore, sustainability for housing is important to achieve balance between economic development, social interactions and environmental impact by reducing the problems related to population growth, urbanisation, slums, poverty, climate change, lack of access to sustainable energy, and economic uncertainty. One of the goals in DRN and Pelan Tindakan Dasar Perumahan Negara (PTDRN) is to provide an affordable housing and ensure the people from low-income can own a house. However, there is an issue towards assessing the sustainability level of affordable housing, especially in social aspects. This study will discuss on sustainability of affordable housing in Malaysia focused on social aspects. Assessment of spatial indicators was conducted to assess the indicator's implementation of social aspect of the sustainability model. The indicators used in this study include public community facilities, health, safety, religion, and public transportation. These indicators will determine the level of sustainability of the affordable housing. From the results, most of the affordable housing in Malaysia is in intermediate level of sustainability in term of social aspects. These results can help/guide the Government in planning and development in the future, especially with collaboration from private agencies and non-government organization (NGO).
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: 08-09-2021
Abstract: Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.
Publisher: IEEE
Date: 07-2019
Publisher: MDPI
Date: 07-2022
Publisher: Informa UK Limited
Date: 20-02-2022
Publisher: MDPI AG
Date: 03-2022
DOI: 10.3390/APP12052570
Abstract: Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be ided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis.
Publisher: MDPI AG
Date: 19-07-2021
Abstract: Weeds compete with crops and are hard to differentiate and identify due to their similarities in color, shape, and size. In this study, the weed species present in sorghum (sorghum bicolor (L.) Moench) fields, such as amaranth (Amaranthus macrocarpus), pigweed (Portulaca oleracea), mallow weed (Malva sp.), nutgrass (Cyperus rotundus), liver seed grass (Urochoa panicoides), and Bellive (Ipomea plebeian), were discriminated using hyperspectral data and were detected and analyzed using multispectral images. Discriminant analysis (DA) was used to identify the most significant spectral bands in order to discriminate weeds from sorghum using hyperspectral data. The results demonstrated good separation accuracy for Amaranthus macrocarpus, Urochoa panicoides, Malva sp., Cyperus rotundus, and Sorghum bicolor (L.) Moench at 440, 560, 680, 710, 720, and 850 nm. Later, the multispectral images of these six bands were collected to detect weeds in the sorghum crop fields using object-based image analysis (OBIA). The results showed that the differences between sorghum and weed species were detectable using the six selected bands, with data collected using an unmanned aerial vehicle. Here, the highest spatial resolution had the highest accuracy for weed detection. It was concluded that each weed was successfully discriminated using hyperspectral data and was detectable using multispectral data with higher spatial resolution.
Publisher: IOP Publishing
Date: 31-07-2018
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: IOP Publishing
Date: 07-2020
DOI: 10.1088/1755-1315/540/1/012074
Abstract: Agriculture technologies have shown potential to improve agriculture management. This awareness has attracted researchers to develop technologies for farmers to ease their daily routine and also to reduce cost of labor and increase the yield production. Smartphone is one of the gadgets that can be useful in agriculture because of its useful functions, portability and affordable. It helps users to find locations, access information and capture images and sound. Smartphone functions can be combined to build versatile mobile agriculture applications. These helps to deliver information about crops and management needs. This paper develops an Android application for smart farming in crop management known as Padi2U. Padi2U is a mobile application developed to help farmers in their paddy field management. The application is developed using Master App Builder, a software that is used to create mobile applications. This application contains information about PadiU Putra paddy variety, agriculture agency, sites information, planting schedule, drone images, paddy disease, pest, weed, weather forecast, yield information and farmer report. All information is described in the native language of the farmers that is the Malay language. Final results of this project is the mobile application which will is useful to manage crops and improve the management. The same method can be applied to different crops in the future.
Publisher: XMLink
Date: 2023
Publisher: MDPI AG
Date: 16-04-2022
Abstract: The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. This review aims to show the current and future trends of mobile computing based on spectral signature analysis for pest and disease management. In this review, the use of mobile applications for pest and disease monitoring is evaluated based on image processing, the systems developed for pest and disease extraction, and the structure of steps outlined in developing a mobile application. Moreover, a comprehensive literature review on the utilisation of spectral signature analysis for pest and disease management is discussed. The spectral reflectance used in monitoring plant health and image processing for pest and disease diagnosis is mentioned. The review also elaborates on the integration of a spectral signature library within mobile application devices to obtain information about pests and disease in crop fields by extracting information from hyperspectral datasets. This review demonstrates the necessary scientific knowledge for visualising the spectral signature of pests and diseases using a mobile application, allowing this technology to be used in real-world agricultural settings.
Publisher: MDPI AG
Date: 14-10-2021
DOI: 10.3390/AGRICULTURE11101004
Abstract: Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.
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: IOP Publishing
Date: 07-2020
DOI: 10.1088/1755-1315/540/1/012091
Abstract: Productions of rice in Malaysia is less compared to other rice producers such as Thailand. One of the reasons is lack of management in weeds. Losses due to weeds must be solved in rice production system, they interfere with the activities involved in the field throughout crop growing period. The main problem in rice field is to detect the presence of weeds among the paddy plant. It is hard to detect from plain sight for weeds in the green field by farmers. This research developed a technique to detect and control the weeds in the rice field. Spectral signature of weeds was collected and stored in the mobile apps for weed management application. The spectral signature of weeds species is unique and has their own characteristics. It can be used as a reference on how to detect weeds in the rice field using remote sensing. In this study, the result outcomes are mainly targeting to help all the paddy farmers in Malaysia to increase their production numbers of paddy. In addition, paddy farmers will be introduced with much advance technique to spot on the weeds that have been infesting in their paddy field and can prepare with what strategy to control on different kind of weeds. Different weed species has different physiological characteristics and it requires different type of strategy to counter the growth in the paddy field. Every weed in the paddy field need to be recognized in order to plan a control strategy. By using only naked eyes of a human and the limited view through the side view of the paddy field contributes to the problem of recognizing type of weeds that infesting the area.
Publisher: Universiti Putra Malaysia
Date: 30-04-2021
Abstract: 2-D electrical resistivity has been a proper investigation survey for determination of subsurface geophysical in describing the complex features geology profile. In this study, an electrical resistivity survey was conducted at paddy cultivation area located in Melor, Kelantan, Malaysia. Since the end plot of paddy field experiences water scarcity especially during dry season, there is a need to find other alternative water source. The study was conducted on 1st and 2nd February 2020 to identify zone area of groundwater for Melor, Kelantan. Four resistivity lines using Induced Polarization (IP) and 2-D Electrical Resistivity Imaging Technique were conducted using a set of ABEM Terrameter SAS4000. Short resistivity survey was applied to gained detail of subsurface formation near the ground, while the longer resistivity survey was applied to obtain deeper subsurface delineation. Measured data obtained was analyzed using RES2DINV software and result of contrast resistivity values was used to determine the geological structures, while the chargeability values were analyzed accordingly to determine area of expected potential groundwater zone. Results from the resistivity profiles show a range values of 0 Ωm to 50,000 Ωm with total maximum acquired depth of 65.6 m below ground surface. The chargeability profiles show a range values of 0 msec to 500 msec, that shows potential of groundwater zone area lies at 0 to 4 msec. It was found that at a depth of 60 to 75 m, 30 m from center of Profile B was suitable for a production well which was expected to be a potential area for groundwater zone.
Publisher: IOP Publishing
Date: 15-04-2019
Publisher: IOP Publishing
Date: 31-07-2018
Publisher: IEEE
Date: 12-2019
Publisher: MDPI AG
Date: 04-12-2022
Abstract: Weed management in large-scale solar photovoltaic (LSS-PV) farms has become a great concern to the solar industry due to scarcity of labour and the ever-increasing price of pesticides, which opens up possibilities for integrated farming, also known as agrivoltaics. Improper weed control may have multiple negative impacts such as permanent shading of the module surface, pest housing which damages communication cables, and even bush fires. The shaded PV modules can be heated up to extreme temperatures, causing costly burn-out damage. Critical information on the types of weeds on solar farms, especially in Malaysia, has not been established to support the concept of weed management. Thus, with this study, detailed composition of the weed community was obtained via quadrat s ling between solar PV modules, near ground equipment, near perimeter fencing, and directly underneath the PV modules. Weed-control measures via high-quality weedmat installation under solar PV arrays have been implemented where this approach can be considered effective on solar farms based on the existing PV structure height and equipment constraints plus the increasing cost for labour and agricultural inputs. This work underlines the proposed Agrivoltaic for Large Scale Solar (Agrivoltaic4LSS) program to complement the solar industry in Malaysia towards an agrivoltaic, eco-friendly approach to weed management.
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 Nik Norasma Che'Ya.