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The present work investigated the potential application of a portable and low-cost spectroscopic technique to predict the soluble solid content (SSC) for determining the maturity level of watermelons. A total of 63 watermelon samples were used in the present work, representing three different maturity levels: unmatured, matured, and over-matured. Before spectral acquisition, each watermelon sample was cut into half, producing 126 fruit portions. Visible shortwave near infrared (VSNIR) spectrometer was used to record the spectral data from the skin surface of each portion. The SSC of each portion was measured using a digital refractometer. Partial least square (PLS) regression method was used to establish both calibration and prediction models to predict the SSC values from the watermelon samples. Support vector machine (SVM) classifier was used to categorise spectral data into the respective maturity levels. Results showed that the coefficient of determination (R2) values for calibration models of unmatured, matured, and over-matured were 0.65, 0.81, and 0.78, respectively. For the prediction model, the R2 values for unmatured, matured, and over-matured were 0.60, 0.74, and 0.76, respectively. The SVM yielded good classification accuracy of 85%. The present work demonstrated that the proposed spectroscopic method could be applied to predict and classify the maturity level of watermelons based on their skin condition.

The spatial resolution of SAR images has been significantly improved in recent years. Conventional geometric correction models are computationally expensive, and require a significant amount of tim...

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.

Spatial patterns are essential for examining the sustainability derived from land systems. Constructing spatial patterns for sustainable land development is now high on the global agenda to guarantee human welfare. However, there is as yet no consensus on the comprehensive framework for optimizing the spatial pattern of land development (SPLD) contrapose a prominent grain-producing area (PGPA). To narrow this gap, we propose a synthetic framework to shape a more reasonable SPLD for a sustainable development strategy by measuring the equilibrium between the production-living-ecological space (PLES) functions and the resource and environment carrying capacity (RECC). Taking a prominent grain-producing area (PGPA) as the object, a case study involving the Jianghan Plain (JHP) in China is conducted, leading to the following novel insights. (i) The quality of PLES and RECC in a PGPA is affected by multiple dimensions: agriculture, ecology, environment, and society. The indices of the PLES function and the RECC have significant spatial heterogeneity. SPLD in regions with fragile ecological environments and strong development is often under overload pressure. (ii) Based on the spatial zoning results of SPLD, the five partitions were taken as the optimized objects, including zones of the eco-economic, model-agricultural, core-living, eco-conservation, and coordinated-development. The land function definition of these five types of zoning covers the production-living-ecological function orientation in a PGPA. (iii) The SPLD optimization framework proposed above has strong universality because it comprehensively considers the multi-dimensional spatial functional needs of PGPA. In this study, an optimization decision framework of SPLD based on measurement and zoning was established for a PGPA. Significantly, the introduced framework is applicable and practical for optimizing SPLD from a sustainable equilibrium perspective, and the findings have considerable implications for sustainable development in prominent grain-producing areas.

The bagworm species of Metisa plana, is one of the major species of leaf-eating insect pest that attack oil palm in Peninsular Malaysia. Without any treatment, this situation may cause 43% yield loss from a moderate attack. In 2020, the economic loss due to bagworm attacks was recorded at around RM 180 million. Based on this scenario, it is necessary to closely monitor the bagworm outbreak at infested areas. Accuracy and precise data collection is debatable, due to human errors. . Hence, the objective of this study is to design and develop a specific machine vision that incorporates an image processing algorithm according to its functional modes. In this regard, a device, the Automated Bagworm Counter or Oto-BaCTM is the first in the world to be developed with an embedded software that is based on the use of a graphic processing unit computation and a TensorFlow/Teano library setup for the trained dataset. The technology is based on the developed deep learning with Faster Regions with Convolutional Neural Networks technique towards real time object detection. The Oto-BaCTM uses an ordinary camera. By using self-developed deep learning algorithms, a motion-tracking and false colour analysis were applied to detect and count number of living and dead larvae and pupae population per frond, respectively, corresponding to three major groups or sizes classification. Initially, in the first trial, the Oto-BaCTM has resulted in low percentages of detection accuracy for the living and dead G1 larvae (47.0% & 71.7%), G2 larvae (39.1 & 50.0%) and G3 pupae (30.1% & 20.9%). After some improvements on the training dataset, the percentages increased in the next field trial, with amounts of 40.5% and 7.0% for the living and dead G1 larvae, 40.1% and 29.2% for the living and dead G2 larvae and 47.7% and 54.6% for the living and dead pupae. The development of the ground-based device is the pioneer in the oil palm industry, in which it reduces human errors when conducting census while promoting precision agriculture practice.

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

Knowing actual nutrient requirements for rice plants is crucial in supplying the correct amount of fertilizer, especially nitrogen since nitrogen is one of the most key limiting nutrients in rice c...

The outbreak of diseases such as the pandemic Covid-19 is a critical problem that has emerged throughout the world. The outbreak is believed to be originated from Wuhan, China in December 2019. In February 2020, World Health Organization declared the name of the virus as Covid-19 and it has become a global pandemic in March 2020. This paper aims to review articles related to the application of GIS spatiotemporal analysis and web-based mapping for the Covid-19 pandemic. GIS spatiotemporal analysis is used to track the pattern of the Covid-19 and web-based mapping-based mapping is used to visualize the Covid-19 cases. This proves that GIS is a helpful tool to determine and track the transmission pattern and visualize the Covid-19 cases so that early mitigation can be done to combat the virus

Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables, and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers, the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy.

Oil palm industry is the main source of income to Malaysia. Traditionally, oil palm fresh fruit bunches (FFB) are classified by oil palm fruit graders based on their experience. This method is subjective and often inconsistent. This paper reports a method to classify oil palm FFB using an optical spectrometer with wavelengths from 180 nm to 1100 nm. Ninety-six oil palm FFB were classified into unripe, ripe, and over ripe classes. Reflectance intensity data from 180 to 1100 nm were collected. Classification accuracies were tested by using all bands from 180 to 1100 nm, principal components, and seven vegetation indices (VI) that were computed from the extracted bands. The classifier applied in this study is Support Vector Machine. The results show that all bands classification achieved the highest accuracy of 90.6% with Kappa coefficient 0.86. Principal components classification shows 73.2% accuracy with 0.59 Kappa coefficient. Among the seven VIs, normalized difference vegetation index 2 (NDVI2) shows the highest accuracy of 84.4% with Kappa coefficient 0.77. These findings provide valuable information to future researchers in this field to develop automatic oil palm FFB classifier.

Internet of Things (IoT) based Aquaculture is an ongoing area of interest in the fisheries sector. The Agriculture 4.0 era is being advanced by IoT technology, yet the Aquaculture 4.0 is a lagging area in many countries. This article presents results obtained so far of an ongoing research of published work highlighting the monitoring of water quality in fishponds. This analysis was performed extensively from May to December 2020 on a total of 28 internationally published research papers. This review is divided into four categories: (1) recent research, (2) aquaculture environment (inland and marine), (3) research approaches for water quality monitoring, (4) common water quality parameters. Much of the published research concentrated on inland aquaculture (81%), while on marine aquaculture species accounted for 19% of papers reviewed so far. The experimental approach (64%) was the most widely practiced type of research in water quality monitoring. There is a need for long term experimental research to identify the challenges and suggest appropriate solutions. In terms of water quality parameters, temperature (35%), dissolved oxygen (34%), and pH (31%) are topmost prioritized water quality parameters. Future work will delve into more relevant papers and expand into the system architecture and the major components of IoT based aquaculture e.g., sensors, technology, data transmission pathway, and power supply. It is hoped that the findings from this review will support the aquaculture industry, researchers, practitioners, and decision makers in the long run. The authors welcome suggestions on relevant papers to be included in this ongoing review.

This study focuses on suitable site identification for constructing a hospital in Malacca, Malaysia. Using significant environmental, topographic, and geodemographic factors, the study evaluated and compared machine learning (ML) and multicriteria decision analysis (MCDA) for hospital site suitability mapping to discover the highest influential factors that minimize the error ratio and maximize the effectiveness of the suitability investigation. Identification of the most significant conditioning parameters that impact the choice of an appropriate hospital site was accomplished using correlation-based feature selection (CFS) with a search algorithm (greedy stepwise). To model the potential hospital site map, we utilized multilayer perceptron (MLP) and analytical hierarchy process (AHP) models. The outcome of the predicted site models was validated utilizing CFS 10-fold cross-validation, as well as ROC curve (receiver operating characteristic curve). The analysis of CFS indicated a very high correlation with R2 values of 0.99 for the MLP model. However, the ROC curve indicated a prediction accuracy of 80% for the MLP model and 83% for the AHP model. The findings revealed that the MLP model is reliable and consistent with the AHP. It is a sufficiently promising approach to the location suitability of hospitals to ensure effective planning and performance of healthcare delivery.

Environmental concerns are growing about excessive applying nitrogen (N) fertilizers, especially in oil palm. Some conventional methods which are used to assess the amount of nutrient in oil palm are time-consuming, expensive, and involve frond destruction. Remote sensing as a non-destructive, affordable, and efficient method is widely used to detect the concentration of chlorophyll (Chl) from canopy plants using several vegetation indices (VIs) because there is an influential relation between the concentration of N in the leaves and canopy Chl content. The objectives of this research are to (i) evaluate and compare the performance of various vegetation indices (VIs) for measuring N status in oil palm canopy using SPOT-7 imagery (AIRBUS Defence &amp, Space, Ottobrunn, Germany) to (ii) develop a regression formula that can predict the N content using satellite data to (iii) assess the regression formula performance on testing datasets by testing the coefficient of determination between the predicted and measured N contents. SPOT-7 was acquired in a 6-ha oil palm planted area in Pahang, Malaysia. To predict N content, 28 VIs based on the spectral range of SPOT-7 satellite images were evaluated. Several regression models were applied to determine the highest coefficient of determination between VIs and actual N content from leaf sampling. The modified soil-adjusted vegetation index (MSAVI) generated the highest coefficient of determination (R2 = 0.93). MTVI1 and triangular VI had the highest second and third coefficient of determination with N content (R2 = 0.926 and 0.923, respectively). The classification accuracy assessment of the developed model was evaluated using several statistical parameters such as the independent t-test, and p-value. The accuracy assessment of the developed model was more than 77%.

Nowadays, monitoring and observing insect pest populations is a major and crucial issue in agriculture, especially for crop protection. Spectroscopic techniques are well recognized for dete...

The classification of oil palm nutrient level based on leaf samples is an important factor to dictate the quality of fresh fruit bunch (FFB). The optimum nutrient level in a palm tree ensures high yield and productivity. This study evaluated a spectroradiometer of spectral bands ranging from 350 to 2500 nm to detect nutrient level in oil palm leaf samples. The features considered were types of nutrient and fronds, explored in spectral reflectance of wavelength for nutrient level determination. Results from statistical analysis using the spectral reflectance of oil palm leaves with partial least square (PLS) models were used for classification of three nutrient levels, comprising of low, optimum, and high amount of fertilization, using the artificial neural network (ANN) to inspect oil palm leaves for contents of nitrogen (N) and potassium (K). From the 90 leaf samples, the ANN models had classification performance of 85.32% accuracy for oil palm nutrient contents determination and 69.42% accuracy for frond identification. Results of this study imply the use of ANN as a prime tool for classification and identification of features in oil palm leaves. Keywords: Artificial neural network (ANN), Oil palm, Nutrients, Spectroradiometer.

The Support Vector Machine (SVM) is a Machine Learning (ML) algorithm which may be used for acquiring solutions towards better crop management. The applications of SVM in precision agriculture (PA) are compared by identifying its interactions with variables, comparing its model performance, highlighting its strengths and weaknesses, as well as suggestions for improvements. From the perspective of six ML applications in PA, we confirmed features which may benefit the model in general (e.g. feature selection) or specific applications (e.g. phenology). SVM was found to outperform most models, with an inconclusive comparison with Random Forest (RF) and inferior to Deep Learning (DL). To our knowledge, this review highlights and summarizes recently renewed efforts of improving SVM performance in PA through its integration with DL, which is believed to be an upcoming trend for ML model development in modern PA.

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized ...

Marine plastic pollution has received global attention consequent on the myriad of challenges associated with it. This study is hinged on the need to mitigate plastic leakage from land-based sources to the ocean. Geospatial technology which consists of multiple thematic maps based on geo-environmental factors, statistical data and consumer‘s behaviour were integrated to model plastic leakage in parts of Selangor State (Kajang, Petaling, Cheras and Semenyih) in Malaysia. Geo-environmental factors such as land use, the study area hydrological networks and topography were considered and prepared in ARCGIS platform to produce Land Use Map, Drainage Density Map, and Digital Elevation Map (DEM) respectively. Statistical data was used to develop the Plastic Waste Density Map (PWDM), and the litter characteristic of the study area was used to develop Plastic Waste Leakage Model (PWLM). The various raster maps were over laid in ARCGIS 10.4 to develop GIS-based plastic leakage model for the study area. To ensure ease in visualization of the variability of the leakage potential, the model was classified from 0 to 1 according to fuzzy scale: 0 to 0.25 was classified as low plastic leakage density, 0.25 to 0.5 was ranked as medium plastic leakage density, while 0.5 to 0.75 and 0.75 to 1 were classified as high and very high plastic leakage density respectively. The leakage density model shows that Cheras and kajang has higher plastic leakage density, while Petaling and Semenyih depicts lower risk to plastic leakage. The plastic leakage density map was validated using litter density values from the major drainages in the area. The novel GIS based research method as used here can be applied to other regions, data on the distribution of plastic leakage potential in this study will be a great tool in plastic leakage mitigation program for the study area.

Oil palm crops are essential for ensuring sustainable edible oil production, in which production is highly dependent on fertilizer applications. Using Landsat-8 imageries, the feasibility of macronutrient level classification with Machine Learning (ML) was studied. Variable rates of compost and inorganic fertilizer were applied to experimental plots and the following nutrients were studied: nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca). By applying image filters, separability metrics, vegetation indices (VI) and feature selection, spectral features for each plot were acquired and used with ML models to classify macronutrient levels of palm stands from chemical foliar analysis of their 17th frond. The models were calibrated and validated with 30 repetitions, with the best mean overall accuracy reported for N and K at 79.7 ± 4.3% and 76.6 ± 4.1% respectively, while accuracies for P, Mg and Ca could not be accurately classified due to the limitations of the dataset used. The study highlighted the effectiveness of separability metrics in quantifying class separability, the importance of indices for N and K level classification, and the effects of filter and feature selection on model performance, as well as concluding RF or SVM models for excessive N and K level detection. Future improvements should focus on further model validation and the use of higher-resolution imaging.

Producing accurate land-use and land-cover LULC mapping is a long-standing challenge using solely optical remote-sensing data, especially in tropical regions due to the presence of clouds. To supplement this, RADARSAT images can be useful in assisting LULC mapping. The fusion of optical and active remote-sensing data is important for accurate LULC mapping because the data from different parts of the spectrum provide complementary information and often lead to increased classification accuracy. Also, the timeliness of using synthetic aperture radar SAR fills information gaps during overcast or hazy periods. Therefore, this research designed a refined classification procedure for LULC mapping for tropical regions. Determining the best method for mapping with a specific data source and study area is a major challenge because of the wide range of classification algorithms and methodologies available. In this study, different combinations and the potential of Landsat Operational Land Imager OLI and RADARSAT-2 SAR data were evaluated to select the best procedure for LULC classification. Results showed that the best filter for SAR speckle reduction is the 5 × 5 enhanced Lee. Furthermore, image-sharpening algorithms were employed to fuse Landsat multispectral and panchromatic bands and subsequently these algorithms were analysed in detail. The findings also confirmed that Gram–Schmidt GS performed better than the other techniques employed. Fused Landsat data and SAR images were then integrated to produce the LULC map. Different classification algorithms were adopted to classify the integrated Landsat and SAR data, and the maximum likelihood classifier MLC was considered the best approach. Finally, a suitable classification procedure was designed and proposed for LULC as mapping in tropical regions based on the results obtained. An overall accuracy of 98.62% was achieved from the proposed methodology. The proposed methodology is a useful tool in industry for mapping purposes. Additionally, it is also useful for researchers, who could extend the method for different data sources and regions.

Performance of different vegetation indices (VIs) in combination with single- and multiple-endmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tr...

Rice is considered the main food source for over 40% of the world population and plays a crucial role in countries’ food security, food management, and economic aspects. The value of SAR remote sensing in agricultural studies has its source of illumination and not limited to cloud cover. This makes it highly preferable over optical sensors in cloud-shrouded countries. The objective of the study is to assess the capability of Sentinel-1 data for determining paddy planting methods, identifying unhealthy paddy and an attempt made to differentiate rice varieties through correlation of in situ measurements and temporal variation of SAR backscattered signals. Six Sentinel-1 images are stacked to cover the entire paddy lifecycle. The correlated field data and plant backscatter showed that transplanted paddy has backscatter higher than broadcasted paddy. Two drops of paddy backscatter coefficient occurred, the first one, at the reproductive stage when paddy was attacked by bacteria and the second drop was at the ripening stage due to the attack of pests. The five rice varieties planted in Seberang Perak, Malaysia had the backscatter with insignificant differences that cannot confirm the Sentinel-1 capability to differentiate planted rice varieties. According to the obtained results, the time series of Sentinel-1 data has the capability for paddy rice growth monitoring.

The effects of plant diseases on agricultural production worldwide contribute to significant economic and post-harvest losses. To maintain the sustainability of the farming sector, the early detection of plant and pathogens is essential. Non - destructive methods for tracking the health conditions of plants in real-time applications are among the most realistic and feasible in this regard. Owing to non - destructive methods and non - contact measuring devices, thermal imaging advancement has become an essential technique in all fields. Thermal imaging operates by emitting infrared irradiation in all materials. The method, therefore, uses radiation to create an image of the thermal distribution of the body surface. This approach is useful for all areas where the difference in temperature helps in the area/object analyses. Therefore, this paper briefly explores the potential for thermal imaging to be used to detect plant disease in the control area and the field.

According to the natural ripeness phenomena of oil palm fruit, the oil palm fresh fruit bunch (FFB) ripeness is divided into three different categories called, under ripe, ripe, and overripe. The current method in the mills for oil palm FFB grading is manually using human graders. However, the manual grading method is subjective and prone to human errors. Thus, accurate classification of oil palm FFB with fast and easy process is necessary in oil palm farms and in the palm oil industry to grade high-quality products, especially when classifying a large amount of fruits. In this paper, an image processing algorithm scenario represented by image acquisition, segmentation, and feature extraction is implemented in real time system for oil palm FFB maturity grading. This study presents an automated inspection for the oil palm FFB using an external grading system processing based on the colour feature extraction technologies; namely, color histogram and statistical color feature (mean and standard deviation), were used to extract the oil palm FFB image’s color features. An artificial neural network (ANN) classifier as supervised machine learning technique is applied to train and test the system performance accuracy based on area under curve (AUC) of the receiver operation characteristic (ROC) as classifier performance evaluation. The result show that the colour histogram technique based on the ANN classifier was observed to be a more robust and accurate classifier, with overall 94% correct ripeness classification accuracy, for oil palm FFB maturity grading compared to the other techniques applied and tested in this study.