Segregation of oil palm fruit ripeness using color sensor

Oil palm is an important industry that has contributed to income and support to the economic sector especially for Malaysia and Indonesia. However, most of the equipment in the oil palm industry is still operated manually. This work developed a system to separate bunches of oil palm fruit using color sensors according to maturity level. Fruit color plays a decisive point in determining fruit maturity. Here, a specific threshold point of red green blue (RGB) was obtained for the determination of the maturity level of oil palm fruit. Point values of < 120, 120 < x < 150 and > 150 represent the maturity levels of unripe, under ripe and ripe, respectively. This paper is the first to report the RGB points for use in the development of automated oil palm segregation system in the oil palm plantation industry. Thus, this paper will pave the way in producing an accurate and reliable oil palm separation system, which in turn has a positive effect in reducing human error. In the future, a set of sensors is proposed to detect a bunch of the oil palm fruits. This further can speed up the segregation process and more suitable for adaptation to the industry.

Identification of a Suitable Machine Learning Model for Detection of Asymptomatic Ganoderma boninense Infection in Oil Palm Seedlings Using Hyperspectral Data

In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124s when run on a personal computer with an Intel® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry.

Distinct Detection of Ganoderma Boninense On Metal Oxides-Gold Nanoparticle Composite Deposited Interdigitated Electrode DNA sensor

Oil palms suffer severe losses due to Ganoderma boninense infection that causes Basal Stem Rot (BSR). The available detection measuring the severity of BSR disease have not proved satisfactory output. Due to the influence of oil palm industry in country’s economy, effective and efficient means of diagnostic measure is mandatory. Among the available diagnostic tools, biosensors were redeemed to yield the most rapid and selective results. To overcome the current issues, herein Interdigitated Electrode (IDE) electrochemical DNA biosensor to detect Ganoderma boninense was successfully designed and fabricated by thermal deposition. Lift-off photolithography fabrication process was applied followed by the surface chemical functionalization via seed deposition. Zinc Oxide (ZnO) and Titanium Dioxide (TiO2) were overlaid and the functionalized metal oxides IDE surfaces were used to detect DNA sequence complementation from Ganoderma boninense. Furthermore, gold nanoparticles were doped to increase the surface to volume ratio and enhance biocompatibility. Characterizations were made by validating the sensor’s topology characteristics and electrical characteristics. From the results recorded, it has been justified that IDE with ZnO doped with gold nanoparticles surface serves as an excellent DNA sensor for the detection of Ganoderma boninense with a remarkable current of 290 nA and 176 nA for immobilization and hybridization respectively.

Synthesis and Characterization of Polyurethanes from Residual Palm Oil with High Poly-Unsaturated Fatty Acid Oils as Additive

In the effort to produce renewable and biodegradable polymers, more studies are being undertaken to explore environmentally friendly sources to replace petroleum-based sources. The oil palm industry is not only the biggest vegetable-oil producer from crops but also one the biggest producers of residual oil that cannot be used for edible purposes due to its low quality. In this paper the development of biopolymers from residual palm oil, residual palm oil with 10% jatropha oil, and residual palm oil with 10% algae oil as additives were explored. Polyols from the different oils were prepared by epoxydation with peroxyacetic acid and alcoholysis under the same conditions and further reacted with poly isocyanate to form polyurethanes. Epoxidized oils, polyols and polyurethanes were analyzed by different techniques such as TGA, DSC, DMA, FTIR and H-NMR. Overall, although the IV of algae oil is slightly higher than that of jatropha oil, the usage of algae oil as additive into the residual palm oil was shown to significantly increase the hard segments and thermal stability of the bio polyurethane compared to the polymer with jatropha oil. Furthermore, when algae oil was mixed with the residual palm oil, it was possible to identify phosphate groups in the polyol which might enhance the fire-retardant properties of the final biopolymer.

A review on the potential of empty fruit bunch (EFB) compost as growing medium for oil palm seedling production

The oil palm industry is among the important sectors in Malaysia. The productions of palm oil keep increasing year by year due to high demand from other countries, generating an abundance of wastes from the field and the mill. These wastes may significantly affect the environment. Composting is one of the methods to reduce the volume of waste. The compost material is widely used especially in agriculture activities due to its properties which have been enhanced during the composting process. Empty fruit bunch (EFB) compost mostly returns to the soil as mulch to conserve soil moisture and acts as organic fertilizer since it contains high nutrients needed by the plant. Currently, the depletion of fertile soil leads to less availability of growing medium, especially in the nursery. The properties of the growing medium are important to ensure better root development of seedlings and subsequently affect the overall plant growth. Therefore, numerous studies have been conducted to identify the suitable growing medium as a substitute for topsoil which is currently limited to raising seedlings in the nursery. This review examines the current methods of composting EFB and provides summarized research information on the effect of EFB compost on oil palm seedling growth. The oil palm wastes that are properly managed could produce value-added by-products and promotes sustainable agriculture practices.

Synthesis of Oil Palm Frond (OPF) Based Silica Nanomaterial via Sol-gel Method with Enhanced Phenol Removal

This research investigates the potential of the oil palm frond (OPF), a well-known biomass from the oil palm industry, as a feasible silica precursor that can be utilised in the removal of phenol from an aqueous solution. Dried OPF was combusted to obtain OPF ash that was treated with citric acid before being synthesised as silica nanomaterial via the sol-gel method. The FTIR results of synthesised silica exhibit a similar peak with commercially available silica. Silica material was then used for phenol removal under different parameters including pH, contact time, dosage, concentration, and temperature, then analysed using UV-Vis Spectrophotometer. The optimum condition was obtained at pH 7 within 45 mins of contact time using 0.2 g/L silica dosage under 10 ppm of phenol concentration at 303 K that aid in enhancing phenol removal by the OPF-based silica. At this condition, silica nanomaterial successfully removed up to 68% of phenol in an aqueous solution with adsorption capacity of the adsorbent is within the range of 34 mg/g. These results demonstrate the potential application of silica nanomaterial from OPF as an adsorbent in phenol removal from wastewater.

Chemical Composition of Parenchyma and Vascular Bundle from Elaeis guineensis

Elaeis guineensis is an alternative source of raw materials for renewable energy in Malaysia. Thus, to enhance the use of the abundant biomass generated by the oil palm industry in Malaysia, a study was conducted in view of exploring the chemical composition such as sugar potential of this industrial byproduct. In this context, oil palm trunks were separated into individual cell that are parenchyma and vascular bundle to investigate the fundamental research about oil palm trunk. The aim of this study was to examine the chemical composition of parenchyma and vascular bundle of oil palm trunk. The oil palm trunk was kept under shade at room temperature of 28–30°C for 0, 45, and 60 days. The chemical composition analysis was carried out according to TAPPI methods. Based on storage time and different part of oil palm trunk, the result has shown that the sugar content was higher in parenchyma compared to vascular bundle and increase at the storage time of 0, 45, and 60 days while amount of starch showed decrease at the same storage time. It shows that conversion or fermentation of starch to sugar occur in oil palm trunk during storage times of 0, 45, and 60 days, respectively.

Challenges of Iot/5g Advancement in the Oil Palm Upstream

5G technology is vital for the development of Internet of Things (IoT) application in Malaysia to enhance the development of the agriculture and plantation industry. The nation is marching towards big data and artificial intelligence. Consequently, having 5G networks merging with IoT technology as a platform would contribute to the country’s gross domestic income up to RM 12.7 billion ringgit. There are tangible and intangible benefits to the oil palm industry if the technologies of 5G/IOT are implemented. In the oil palm industry, the technology of 5G network can uplift the current technology in the field to enhance the labour productivity. On the other hand, the dependency on foreign workers can also be reduced. Apart from that, the use of 5G/IoT will improve crop productivity and land use efficiency to improve palm oil yield. However, IoT platforms relying on 5G technology will be challenging in rural oil palm areas where there is low signal for communication. In relation to that, preliminary research shows that LoRA technology that is similar to 5G technology is able to work in remote areas. 5G/ IoT related technology implementation is vital in the oil palm plantation at the upstream level.

Identification of Oil Palm’s Consistently Upregulated Genes during Early Infections of Ganoderma boninense via RNA-Seq Technology and Real-Time Quantitative PCR

Basal stem rot (BSR) disease caused by pathogenic fungus Ganoderma boninense is a significant concern in the oil palm industry. G. boninense infection in oil palm induces defense-related genes. To understand oil palm defense mechanisms in response to fungal invasion, we analyzed differentially expressed genes (DEGs) derived from RNA-sequencing (RNA-seq) transcriptomic libraries of oil palm roots infected with G. boninense. A total of 126 DEGs were detected from the transcriptomic libraries of G. boninense-infected root tissues at different infection stages. Functional annotation via pathway enrichment analyses revealed that the DEGs were involved in the defense response against the pathogen. The expression of the selected DEGs was further confirmed using real-time quantitative PCR (qPCR) on independent oil palm seedlings and mature palm samples. Seven putative defense-related DEGs consistently showed upregulation in seedlings and mature plants during G. boninense infection. These seven genes might potentially be developed as biomarkers for the early detection of BSR in oil palm.

Hexaconazole-Micelle Nanodelivery System Prepared Using Different Surfactants for Ganoderma Antifungal Application

Reports on fungicide-based agronanochemicals in combating disastrous basal stem rot disease in the oil palm industry are scant. Herein, we describe the potential of fungicide nanodelivery agents based on hexaconazole-micelle systems produced using three different surfactants; sodium dodecylbenze sulfonate (SDBS), sodium dodecyl sulfate (SDS) and Tween 80 (T80). The resulting nanodelivery systems were characterized and the results supported the encapsulation of the fungicide into the micelles of the surfactants. We have investigated in detail the size-dependent effects of the as-synthesized micelles towards the inhibition growth of Ganoderma Boninense fungi. All the nanodelivery systems indicate that their size decreased as the surfactant concentration was increased, and it directly affects the fungal inhibition. It was also found that Tween 80, a non-ionic surfactant gave the lowest effective concentration, the EC50 value of 2, on the pathogenic fungus Ganoderma boninense compared to the other anionic surfactants; SDBS and SDS. This study opens up a new generation of agronanofungicide of better efficacy for Ganoderma disease treatment.