Locating Suitable Zones for Beekeeping in Selangor Malaysia.

Modern beekeeping has been established in Malaysia since 1981 under the collaborative research and development of the Malaysian Beekeeping Research and Development Team (MBDRT), which was funded by International Dutch Research Council (IDRC). During MBDRT research several findings on the of beekeeping implementation in Malaysia have been compiled such as the list of bee plants, prospect ability of the industry and modernisation of beekeeping techniques. Although type of bee plant that supply nectar and pollen which are favourable to honeybees has been identified, the location of the source has not been identified yet and there is no map for suitable beekeeping location or zones especially using GIS and multi-criteria decision analysis technique. This research demonstrates the application of Geographical Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) technology as a tool to aid decision-making process with particular case study of locating a beekeeping zone in the state of Selangor. In this research land suitability analysis is carried out with respect to the bee’s biotic needs and some other important factors in apiary management. The results of the two techniques for AHP with GIS analysis namely refereed VBA Macro in ArcGIS and prominent Weighted Overlay function are presented, compared and discussed with verification of ground truth data. The integration of AHP model with GIS provides zones of Non-Suitable, Most Suitable, Moderately Suitable and Suitable areas for beekeeping activity in Selangor. The total of Non Suitable Area (NS) is 34.73%, leaving the balance of potential areas of 65.27%. The remaining are the Most Suitable Area (S1) 13.72 %, Suitable Area (S2) of 27.24% and Moderately Suitable Area of 24.32 %

Apicultural site zonation using GIS and multi-criteria decision analysis

This paper discusses the application of Geographical Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) technology as a tool to aid decision-making in a case study to locate beekeeping zones in the state of Selangor. The combination of GIS capabilities with MCDM technique provides greater effectiveness and efficiency of decision making while solving spatial decision problems. In this research, land suitability analysis and zoning was carried out with respect to the bee's biotic needs and some other important factors in apiary management. Suitability weighting was determined using the pairwise comparison matrix of the Analytical Hierarchy Process (AHP) and suitability score using Weighted Overlay function in ArcGIS9. The overall consistency ratio value of AHP pairwise comparison was 0.01 which indicates a reasonable level of consistency in the deployment of the pairwise comparisons. The results of the analysis are presented and verified with actual data of the existing apiaries in Selangor. The integration of AHP model with GIS resulted in Non-Suitable, Most Suitable, Moderately Suitable and Suitable beezones. The total Non Suitable Areas (NS) was 34.73%, leaving the remainder as potential areas (65.27%). The remaining are the Most Suitable Areas (S1) 13.72 %, Suitable Areas (S2) of 27.24% and Moderately Suitable Areas of 24.32 %

Urban sprawl literature review: definition and driving force

The concept of urban sprawl spans multiple dimensions indicating how urban built-up land cover adds up throughout exurban landscapes. These different dimensions of urban sprawl require a re-examination of definitions and their driving forces because certain policies were formulated from the proposition of informed knowledge and have implicitly gratified urban sprawl into adjoining urban peripheries. This article aimed to offer an alternative perspective on urban sprawl, contributing to a better comprehension of its definition and driving forces. The revision of urban sprawl definitions into six categories have been done based on their repercussion, unaesthetic design, driving force, undesirable pattern, extended character, and their consequences to the environment, to assist in giving an in-depth understanding of urban sprawl in order to implicate effective policy actions. A revision of the driving forces of urban sprawl into various socioeconomic, institutional, demographic, market and technological factors further support the research on spatial planning and urban growth. It is conducted through a detailed discussion and analysis of evidence retrieved from wide-ranging urban studies literature. An informed decision through understanding the driving force of urban sprawl and addressing the root cause can produce a twofold benefit of socio-environment wellbeing and growth-friendly policy initiatives

Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data.

Although hyperspectral remote sensing has been used to study many agricultural phenomena such as crop stress and diseases, the potential use of this technique for detecting Ganoderma disease infestations and damage to oil palms under field conditions has not been explored to date. This research was conducted to investigate the feasibility of using a portable hyperspectral remote-sensing instrument to identify spectral differences between oil-palm leaves with and without Ganoderma infections. Reflectance spectra of samples representative of three classes of disease severity were collected. The most significant bands for spectral discrimination were selected from reflectance spectra and first derivatives of reflectance spectra. The significant wavelengths were identified using one-way analysis of variance. Then, a Jeffries–Matusita (JM) distance measurement was used to determine spectral separability between the classes. A maximum likelihood classifier method was used to classify the three classes based on the most significant wavelength spectral responses, and an error matrix was finally used to assess the accuracy of the classification

The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings

Field spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress and disease. The objective of this study was to develop spectral indices for detection of Ganoderma disease in oil palm seedlings. The reflectance spectra of oil palm seedlings from three levels of Ganoderma disease severity were acquired using a spectroradiometer. Denoizing and data transformation using first derivative analysis was conducted on the original reflectance spectra. Then, comparative statistical analysis was used to select significant wavelength from transformed data. Wavelength pairs of spectral indices were selected using optimum index factor. The spectral indices were produced using the wavelength ratios and a modified simple ratio method. The relationship analysis between spectral indices and total leaf chlorophyll (TLC) was conducted using regression technique. The results suggested that six spectral indices are suitable for the early detection of Ganoderma disease in oil palm seedlings. Final results after regression with TLC showed that Ratio 3 is the best spectral index for the early detection of Ganoderma infection in oil palm seedlings. For future works, this can be used for the development of robust spectral indices for Ganoderma disease detection in young and mature oil palm using airborne hyperspectral imaging

How can unmanned aerial vehicles be used for detecting weeds in agricultural fields?

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

Urban sprawl assessment using time-series lulc and NDVI variation: A case study of Sepang, Malaysia

Rapid urbanization that caused urban sprawl is a major worldwide concern. In this study an assessment of urban sprawl was carried out based on Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) variation in Sepang, Malaysia. The land cover classification consisted of Built-up Area (BUA), Vegetation Area (VA), Open Space Area (OSA), and Water Bodies (WB) from 1990 to 2018. Supervised classification based on maximum likelihood techniques were used to identify the land use classes. Based on the analysis of LULC, the majority of VA (i.e. forest field) was transformed into OSA and gradually the land was converted into BUA. Observation within 25 years, supported by NDVI and NDBI has discovered a consistent increase of BUA while contrastingly a decline of VA, while WB and OSA are suspected to have inconsistently varying highs and lows. This study has demonstrated that urban sprawl caused by rapid urbanization has not favour the population. Without proper planning and growth control, urban sprawl in Sepang would have undesirable consequences to the quality of life and the environment. Therefore, comprehensive land use and progressive environmental change can serve as prognostic measures to mitigate urban sprawl, and to achieve sustainable urbanization and to carry out effective planning and decision making. © 2019, ALÖKI Kft., Budapest, Hungary

Urban sprawl literature review : definition and driving force

The concept of urban sprawl spans multiple dimensions indicating how urban built-up land cover adds up throughout exurban landscapes. These different dimensions of urban sprawl require a re-examination of definitions and their driving forces because certain policies were formulated from the proposition of informed knowledge and have implicitly gratified urban sprawl into adjoining urban peripheries. This article aimed to offer an alternative perspective on urban sprawl, contributing to a better comprehension of its definition and driving forces. The revision of urban sprawl definitions into six categories have been done based on their repercussion, unaesthetic design, driving force, undesirable pattern, extended character, and their consequences to the environment, to assist in giving an in-depth understanding of urban sprawl in order to implicate effective policy actions. A revision of the driving forces of urban sprawl into various socioeconomic, institutional, demographic, market and technological factors further support the research on spatial planning and urban growth. It is conducted through a detailed discussion and analysis of evidence retrieved from wide-ranging urban studies literature. An informed decision through understanding the driving force of urban sprawl and addressing the root cause can produce a twofold benefit of socio-environment wellbeing and growth-friendly policy initiatives

Non-invasive determination of chlorophyll content in oil palm seedlings using field spectroscopy.

Chlorophyll content and greenness index are frequently used as a basis in determining the vigor and health of vegetation and explaining the physiological and pathological properties of plants. This paper evaluates the estimation of chlorophyll content in oil palm seedlings in nursery using destructive and non-destructive sampling and their relationships with the foliar hyperspectral indices. Foliar reflectance was measured using GER1500 spectroradiometer concurrent with in situ measurement of foliar chlorophyll and sampling. Foliar chlorophyll was acquired using Minolta Chlorophyll Meter SPAD-502 and Total Leaf Chlorophyll (TLC) of foliar was determined using the spectrophotometry method. The spectra of oil palm were transformed using published indices which have been ascertained as an excellent indicator to indicate foliar chlorophyll and plant vigorousness. The SPAD readings were correlated with oil palm leaf chlorophyll contents extracted in the laboratory to establish the calibration equations for the computation of the TCL, Chlorophyll-a (Chl-a) and Chlorophyllb (Chl-b). The coefficient determination (R2) of 0.763, 0.781 and 0.460 were obtained respectively; with root mean square error between 0.064-0.147 μm/cm2. Linear and non-linear regression were analysed to study relationship between SPAD, TLC, Chl-a and Chl-b with hyperspectral indices. Linear regression was found the best in explaining correlation with the selected chlorophyll indices

The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review

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 divided 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