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Rice is a crucial food crop in Malaysia, being the staple food of the 31 million residents. However, the country’s annual rice production of 1.8 million MT can fulfill only about 70 percent of the national self-sufficiency level, while the remaining 30 percent is still being imported from other countries. Over the years, the Malaysian government has introduced several strategies to enhance the efficiency, productivity, and cropping intensity of the rice industry in the country. Such initiatives include promoting the full utilization of modern mechanization and automation technologies. Many studies have shown that adopting appropriate mechanization could minimize production costs, increase the yield, and improve product quality. Soil tillage and grain harvesting are the most energy-demanding and time-consuming operations in paddy production. As such, these two field operations have been fully mechanized in Malaysia. However, although mechanization can increase the productivity of paddy production, using heavy machines such as tractors and combine harvesters also poses challenges. For one, using such heavy machinery can cause soft soil condition and soil compaction and can damage the soil of the paddy field due to the high ground contact pressure pneumatic rubber tires of the machinery. Furthermore, using rubber tires on field machines leads to poor tractive performance and trafficability due to high slippage, thus increasing operating costs. Using a large combine harvester during the harvesting period can also cause several problems such as soil compaction, hardpan breakage, higher operational costs, higher grain losses, and limited access to a small paddy field. Moreover, a large combine harvester is difficult to transport from one place to another. The continuing revolution in mechanization technology has led to the introduction of new machinery for soil tillage and paddy harvesting, which can potentially overcome the existing challenges in mechanized paddy product

White root disease (WRD) infection in rubber plantations which is caused by Rigidiporus micropores can lead to a significant yield loss. At the early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Thus, this study was carried out to investigate the potential application of spectroscopic technology and machine learning algorithms to classify severity level of infected trees at early stage based on spectral data. A total of 50 leaf samples were used in this work, representing five severity levels; healthy, light, moderate, severe and very severe infection. A visible shortwave near-infrared (VSNIR) spectrometer was used to record the spectral data of the leaf samples. The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). The result of the study demonstrates good classification accuracy of 90, 82, 78, and 72% for ANN, SVM, kNN and RF, respectively. This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves.

first_pageDownload PDFsettingsOrder Article Reprints Open AccessReview Sensing Technologies for Measuring Grain Loss during Harvest in Paddy Field: A Review by Muhammad Isa Bomoi 1,2,*ORCID,Nazmi Mat Nawi 1,3,4ORCID,Samsuzana Abd Aziz 1,3 andMuhamad Saufi Mohd Kassim 1,3,4 1 Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Malaysia 2 Scientific Equipment Development Institute, Minna 920001, Nigeria 3 SMART Farming Technology Research Centre, Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Malaysia 4 Institute of Plantation Studies, University Putra Malaysia (UPM), Serdang 43400, Malaysia * Author to whom correspondence should be addressed. AgriEngineering 2022, 4(1), 292-310; https://doi.org/10.3390/agriengineering4010020 Received: 29 January 2022 / Revised: 13 February 2022 / Accepted: 1 March 2022 / Published: 9 March 2022 Downloadkeyboard_arrow_down Browse Figures Review Reports Versions Notes Abstract A combine harvester has been widely employed for harvesting paddy in Malaysia. However, it is one of the most challenging machines to operate when harvesting grain crops. Improper handling of a combine harvester can lead to a significant amount of grain loss. Any losses during the harvesting process would result in less income for the farmers. Grain loss sensing technology is automated, remote, and prospective. It can help reduce grain losses by increasing harvesting precision, reliability, and productivity. Monitoring and generating real-time sensor data can provide effective combine harvester performance and information that will aid in analyzing and optimizing the harvesting process. Thus, this paper presents an overview of the conventional methods of grain loss measurements, the factors that contribute to grain losses, and further reviews the development and operation of sensor components for monitoring grain loss during harvest. The potential and limitations o

Oil palm has become one of the largest plantation industries in Malaysia, but the constraints in terms of manpower and time to monitor the development of this industry have caused many losses in terms of time and expense of oil palm plantation management. The introduction to the use of drone technology will help oil palm industry operators increase the effectiveness in the management of oil palm cultivation and production. In addition, knowledge gaps on drone technology were identified, and suggestions for further improvement could be implemented. Therefore, this study reviews the application and potential of drone technology in oil palm plantation, and the limitation and potential of the methods will be discussed.

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 potential use of natural fibre extracted from oil palm empty fruit bunches has gained wide attention among researchers. This natural fibre comes from fibrous strands which form fibre bundle after shredding process at a mill. The measurement of tensile properties is important to understand the mechanical performance of this fibre bundle. This study was undertaken to determine the tensile properties of the fibre bundle from oil palm empty fruit bunch (OPEFB). Fibrous strands of the OPEFB extracted from shredded empty fruit bunches were twisted to form fibre bundle specimens at different diameters varying from 1 to 5 mm. The tensile properties measured in this study including tensile strength, tensile load and tensile modulus. The measurements were performed using Instron Universal Test Machine (IUTM) model 5000. From the results, it was found that the specimens at 1 and 5 mm in diameter required 71.25 and 429.68 N of the tensile load to break, respectively. The specimen with 1 mm in diameter recorded the highest tensile strength of 90.72 MPa while the specimen with 5 mm in diameter recorded only 21.88 MPa. The highest tensile modulus with value of 662.50 MPa was obtained from the specimen with 1 mm in diameter while the specimen with 5 mm in diameter had the tensile modulus value of 157.47 MPa. It was also found that the tensile strength and tensile modulus decreased when the diameter of the specimens increased. The findings reported in this study can serve as an engineering basis for the design specification in the development of the future in-silo composting machine.

The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers; thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was divided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classi

This paper focuses on the in-field transporting machines currently used in oil palm plantations in Malaysia. It highlights the field conditions, the capacity of the machines and the time of completion of the transporting process which help to identify the determinants of the development of agricultural mechanization in Malaysian oil palm plantations. Understanding of the issues will help facilitate the elimination of weaknesses and defects prior to the manufacturing or modification stage. This will assist designers and manufacturers in the development and production of improved and more efficient field transportation equipment.

Basal stem rot (BSR) is one of the diseases that threaten the oil palm plantations in Southeast Asia, particularly in Malaysia and Indonesia. As the oil palm plantations continue to grow, there is a need for time-effective, non-destructive, and more precise techniques for detecting BSR. Dielectric spectroscopy has been proven to be an effective method for noninvasive classification of BSR in oil palm trees. However, due to the nature of the large spectral data for spectroscopy analysis, there is a need to reduce the data without losing the main features for more efficient computation. This study investigated the feasibility of applying genetic algorithm (GA) as a feature selection algorithm to select the most significant frequencies of dielectric spectral data for identifying BSR disease in oil palms. Then, the data at the most significant frequencies were used as the input of four classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (kNN), and naïve Bayes (NB). The results showed that the best classification accuracy was achieved using LDA classifier with the accuracy of 86.36%. Without implementing GA, the highest classification accuracy was obtained by using the QDA classifier with an accuracy of 82.22%. These results demonstrate the advantages of applying GA as a feature selection model to enhance spectral classification in the identification of BSR in oil palms using dielectric spectroscopy measurements.

Pesticide residue in leafy vegetables like a cabbage can cause harmful effects to consumers. Thus, early detection and classification of pesticide residue could help consumers to choose residue-free cabbages. This research was performed to evaluate the performance of different classification methods to classify spectral data collected from 60 pesticide-free cabbage samples. Deltamethrin pesticide was sprayed on the samples at different dilution concentrations namely pesticide-free (PF), pesticide-low (PL), pesticide-medium (PM) and pesticide-high (PH). The spectral data of the cabbages was recorded using a spectrometer with an effective wavelength in the range of 400 to 1000 nm. The concentration of the pesticide residues in each cabbage sample was quantified using a gas chromatography with an electron detector (GC-ECD). Three classification methods investigated in this study were artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR). The results show that LR, SVM and ANN yielded excellent classification accuracies of 95, 88 and 87%, respectively. This study revealed that the spectroscopic measurement coupled with classification methods are promising technique for detecting and classifying pesticides residues in cabbage samples.

Coconut plantation has the potential to contribute for biomass energy from its waste such as coconut husks and shells. This research aimed to determine the calorific value of coconut shells and husks at different maturity stages and its relationship with moisture content as the first step in determining their acceptability as alternative fuel sources. A bomb calorimetry procedure was performed to measure gross calorific values (GCV) which was used to indicate the potential of the samples to produce biofuels. It was found that the coconut shell had the highest calorific value of 22.36MJ/kg at maturity stage 4 (eleven to twelve months of age) followed by inner husk at 18.96MJ/kg and outer husk at 17.65MJ/kg. The relationship between the average GCV and maturity stages of the whole samples yielded the regression of R2=0.971. This result shows that the average GCV increased as the maturity stages increased. While the mean calorific value obtained from the shells was 16.38MJ/kg which was comparable to certain wood species. The coconut shells, which are generally not fully utilized, abandoned, and discarded, have the potential to be used as energy sources, whilst the husks have a lesser calorific value but could be used as fuel for less energy intensive uses.

In order to reduce excessive fertiliser application, a non-destructive method of spectral data acquisition using spectroradiometer with wavelet analysis was explored to determine the level of nutrients in the oil palm leaves. In spectral data analysis, wavelet de-noising (WD) can be applied to remove background noises and other disturbances such as scattered light that may affect the results of data. Therefore, this study aims to determine and evaluate the best combination of parameters for WD, with respect to nutrients nitrogen (N), phosphorus (P) and potassium (K). These nutrients were studied for three age groups of immature, mature, and old palms. The results were evaluated based on the highest value of coefficient of determination (R2) and lowest root mean square error (RMSE) of partial least square regression (PLSR) analysis. The prediction of nutrient content correlation was found to have tremendous improvement using the proposed technique when compared to the original spectra, with highest prediction R2 value of 0.99 for K of mature palms, 0.97 for N of immature palms and 0.95 for P of mature palms. The results of WD for nutrients prediction were found to be better than results from chemometric method of namely multiplicative scatter correction (MSC). It was observed that for each nutrient type and palm maturity level, there were different combination of parameters based on the highest R2 value that best suited them. Therefore, spectroradiometer assisted with optimal wavelet de-noising parameters gives excellent relationship between spectral data and nutrients N, P, and K.

Agriculture remains as one of the important economic sectors in Malaysia which provides an employment for more than 1.6 million people. However, the growth of this sector may be hampered by a small-scale production, limited technological application, declining number of arable lands, environmental degradation due to climate change, rapid urbanization and aging farmers. In order to improve the competitiveness of the agricultural sector, farmers are encouraged to fully utilise modern technologies in their farms. In this context, adoption of industrial revolution 4.0 (IR4.0) in agricultural sector could bring many benefits, especially in minimizing the production costs and improving the quality of the products. Thus, this review focuses on the adoption strategies of IR4.0 into agricultural sector in Malaysia. A suitability of enabling technologies such as IoT, autonomous robot, big data analytics and artificial intelligent which are pillars for IR4.0 are individually evaluated. The readiness of agricultural industry in Malaysia to embrace this new concept is also discussed. The review also investigates the potentials and possible challenges would be faced by the industry in embracing IR4.0. The recommendations are also provided for farmers, industrial players and policy makes to makes sure a smooth adoption of IR4.0 into agricultural sector in Malaysia.

Measurement of human energy expenditure during crop production helps in the optimization of production operations and costs by identifying steps which that can benefit from the use of appropriate mechanization technologies. This study measures human energy expenditure associated with all 6 major rice (Oryza sativa L.) cultivation operations using two measurement methods-i.e. conventional human energy expenditure method and direct measurement with a Garmin forerunner 35 body media. The aim of this study was to provide a detailed comparison of these two methods and document the human energy costs in a manner that will identify steps to be taken to help optimize agricultural practices. Results (mean + 95%CL) revealed that the total human energy expenditure obtained through the conventional method was 25.5% higher (33.3 ± 1 versus 26.6 ± 1.3) in transplanting and 26.1% higher (30.3 ± 1.9 versus 24.0 ± 2.1) than the human energy expenditure recorded using the Garmin method in broadcast seeding method. Similarly, during the harvesting operation, the conventional measurement and Garmin measurement methods differed significantly, with the conventional method the human energy expenditure was 89.9% higher (3.2 ± 0.4 versus 1.68 ± 0.2) in the fields using the transplanting and 88.7% higher (3.3 ± 0.5 versus 1.8 ± 0.3) in the fields using the broadcast seeding than the human energy expenditure recorded using the Garmin method. When using Garmin method, the human energy expenditure in the case of using the midsize combine harvester was 13.49% lesser (592.4 ± 67.2 versus 522.0 ± 75.1) than the case of using conventional one. Results based on heart rate also indicated that operations such as tillage were less intensive (72 ± 3.3 bpm) compared with operations such as chemicals spraying (135 ± 4 bpm). Although we did not have a criterion measure available to determine which method was the most accurate, the Garmin measurement gives an estimate of actual physical human energy expende

A mid-size combine harvester with 2.76 m reaping width and 103.53 hp engine output has been employed in grain corn production, especially by small-scale grain corn farmers. This study attempted to determine field performances of a typical mid-size combine harvester by measuring its effective field capacity (EFC), field efficiency (FE), fuel consumption (FC) and field machine index (FMI). Different types of energy inputs such as fuel, machinery, human, included direct, indirect, renewable and non-renewable energy involved in grain corn harvesting were also measured. The field measurements were carried out in 3 ha of grain corn farm, under similar field conditions using a typical mid-size combine harvester. The average values of EFC, FE, FC and FMI for the mid-size combine harvester were found to be 0.23 ha/h, 34.97%, 37.25 lit/ha and 0.91, respectively. The average equivalent energy values of fuel, machinery and human energy were 1780.70 MJ/ha, 587.73 MJ/ha and 8.53 MJ/ha, respectively. The average values of the direct and indirect energy were 1789.23 MJ/ha and 587.73 MJ/ha, respectively. The average values of renewable and non-renewable energy were recorded at 8.53 MJ/ha and 2368.42 MJ/ha, respectively. The mid-size combine harvester investigated in this study exhibited good field performance characteristic using a reasonable amount of energy consumption as compared to harvesting operation for other grain crops. From the results, it can be concluded that good practice in harvesting operation could improve field performance, and minimise operational costs and energy consumption.

Agricultural machinery utilization in Malaysia still very low, especially in sweet corn cultivation, compared with the other crop production systems. In this study was estimated two methods to evaluate the mechanization status of the respective field operations in sweet corn cultivation in Malaysia. The first method was used the PCL-HRL-EGL Cartesian plot based on Production capacity, Heartbeat rate, and Energy expenditures of human labor. The second method was used mechanization index based on energy expenditures of machinery and human labor. The study aim of was to assess the mechanization status in sweet corn cultivation in Malaysia. This paper described the overall mechanization status in the cultivation of sweet corn in Malaysia, and the machinery energy, worker’s energy expenditure, and heart rate for various field operations that were involved in cultivation. The field operations include tillage, planting, fertilizing, spraying, harvesting, and cutting plants. Field capacity, and machinery energy for each of the operations were calculated. The calculated mechanization index was used in the study to describe the mechanization status in sweet corn cultivation. A mean overall mechanization index of 36.49% and aggregate machinery energy of 340.67 ± 41.99 MJ/ha were registered for the crop. Highest mechanization index, and machinery energy were acquired in the tillage operation of 94.09% and 105.35 ± 9.37 MJ/ha while the lowest mechanization index, and machinery energy were in the harvesting operation with bags of 0.83%, and 0.42 ± 0.09 MJ/ha. These calculated mechanization indexes, and PCL-HRL-EGL Cartesian Plot were used for ranking the field operations based on their priority for mechanization. The outcome of this research could be contributed to lightening human energy expenditures and improving the mechanization status ultimately.

A total of 42 crops were categorized into eleven Indicative Crop Classification (ICC) in accordance to the Food and Agriculture Organization (FAO) as cereal, vegetables, fruit, nut, oilseed crops, root/tuber crops, beverage and spice crops, sugar crops, leguminous crops, fiber crops and Tobacco for the study of energy utilization in their productions. The energy utilization in the production of these selected crops was taken from the literature studies that were conducted from the collection of 120 published journal articles from 2004 to 2017. Among the eleven crop classifications, the energy input for vegetable and melon crops was the highest (73.425 GJ/ha) while energy input for leguminous crops was the lowest (6.13 GJ/ha). Electricity, fertilizer and diesel are the major sources of energy they contributed by 46%, 20% and 14% respectively. The electricity was mostly for the water pumps that were used for pumping of water for the crops in the field. Direct energy contributed by 39.35% of the total energy consumed while indirect energy contributed 45.19%. Renewable energy represented 17% of total energy used while non-renewable energy represented 83%. For cereal crops, fertilizer energy contributed the highest value in the energy input with a value of 617080.0 MJ/ha or 27% of the total input, direct energy contributed 57% of the total energy input and indirect energy is 43% of the total energy, while renewable and nonrenewable energy shared by 19% and 81% of the total energy input, respectively. On the other hand, the average mechanization index level for all crop classifications was calculated to be 0.52, and these indexes varied from 0.18 for spice crops to 0.77 for cereals crops with corn scoring the highest mechanization index of 0.90 while rice has the lowest index of 0.61. Tobacco has the lowest value of the energy ratio by 0.10 while coconut has the highest value of 29.4. Finally, in energy productivity, watermelon has the highest valueof 1.7 kg/MJ while toba

Oil palm fruitlets are highly susceptible to mechanical damage during harvesting and handling. In this study, the effect of crop parameters and two loading orientations on mechanical properties of oil palm fruitlets were investigated. The investigated crop parameters were four mass categories (11–11.9, 12–12.9, 13–13.9 and 14–14.9 g), two varieties (Tenera and Dura) and three ripeness stages (underripe, ripe and overripe). For impact loading, both vertical and horizontal loading orientations were studied. The investigated mechanical properties were rupture force, rupture energy, deformation at rupture and specific deformation. The mechanical properties were measured using a pendulum impact load test device. A total of 540 fruitlet samples extracted from 18 fresh fruit bunches (FFB) were employed. The results showed that the values of rupture force and energy for all mass categories under the vertical loading orientation were lower than their values under the horizontal loading orientation. The maximum rupture forces of 697.1 and 939.4 N were recorded for the highest mass category (14–14.9 g) under the vertical and horizontal loading orientations, respectively. The results also showed that the values of all mechanical properties belong to Tenera variety were higher than that of Dura variety under the both loading orientations. In terms of the ripeness stage, the rupture force and energy of oil palm fruitlets decreased as the ripeness stage increased. The maximum rupture force and energy of 940 N and 2.95 Nm were recorded for ripe stage under the horizontal loading orientation, respectively. The results from this study would be very useful in designing a proper harvesting and in-field transportation systems to minimize crop damage due to impact loading.

Crude palm oil (CPO) is the main product of oil palm processing and is obtained through a series of unit operations. In a palm oil mill, the main unit operations involve applying saturated steam in the steriliser and digester. The high temperature and water content used will induce thermal hydrolysis, leading to accumulation of free fatty acids (FFAs), the levels of which are considered a major quality index of CPO. A detailed study of the thermal hydrolysis of CPO at different temperatures and water contents in a closed-vessel system was carried out. The results show that there was an increase in FFA accumulation as temperature and initial water content increased. A kinetic model of the hydrolysis was constructed taking into account heat and mass transfer phenomena. The important parameters of the model were the reaction frequency factor (k0T) and the power factor of water fraction (n), which were successfully estimated by gPROMS ModelBuilder at 2.55 × 10–6 (m3/kmol)0.85 min−1 and 0.62, respectively. A sensitivity analysis revealed that the simulated profiles (i.e. FFA content and vapour mass flux, W˙WV) were highly sensitive to these parameters. The model can be a useful tool in the further redesign and quality improvement of the industrial of CPO extraction processes.

Moisture content of a jackfruit is one of the main attributes used by farmers to determine the maturity level of the fruit. The objective of this preliminary research was to explore the potential application of low-cost shortwave near infrared (VSWNIR) spectroscopy to non-destructively predict moisture content of jackfruit from their outer skin. A total of 870 skin portions collected from twenty-nine jackfruit samples were used in this study. After the spectral measurement, the skin portions were dried in the oven in order to measure their moisture content (%, wet basis, w.b.). Partial least square (PLS) method was used to develop both calibration and prediction models for calibrating the spectral data with the moisture content. This study found that the value of coefficient of determination (R2) and root means square error of calibration (RMSEC)were 0.65 and 2.17, respectively. For the prediction model, the value of R2and root mean square error of prediction (RMSEP) were 0.64 and 2.81, respectively. These results indicated the VSWNIR spectrometer is a promising technology for non-destructively predicting moisture content of jackfruits.

The analysis of energy and greenhouse gas emissions in this study was made in respect to both planting methods, transplanting and broadcast seeding methods to investigate the energy pattern of rice production in Malaysia. The field under transplanting method in the main season showed 8.72% lesser mean total energy input, 6.25% higher mean machinery energy, 55.06% lesser mean seed energy and 23.01%higher mean output energy than the field under broadcasting method. Fertilizer the highest contributor of energy inputs it contributed by 62% in both transplanting and broadcasting methods and fuel was the second-highest contributor. The share of direct and indirect energy in the fields under the transplanting method were 19% and 81% and in the fields under the broadcasting method were 17% and 83%respectively. While the share of renewable and non-renewable energy in the fields under the trans-planting method were 7% and 93% and in the fields under the broadcasting method were 14% and 86%respectively. The harvesting operation has the highest mechanization index level (0.99) in both methods. While fertilizing and planting operation have the lowest mechanization index level. The mean energy ratio was 35.68% higher in transplanting than broadcasting (8.1 and 5.97). Fertilizer represents the highest contributor to the total GHG emissions in the two cultivation methods, transplanting and broadcasting and it represents 44% and 42% respectively. Among different types of chemical fertilizers, Nitrogen represents the higher contributor.

In paddy cultivation, harvesting is the most important operation, which needs suitable machinery. Thus, this study was carried out to compare field performances and energy and environmental effect between the conventional 5 m cutting width NEW HOLLAND CLAYSON 8080, 82 kW@2500 rpm combine harvester running on a total net area of 42.78 ha of plots for two rice (Oryza sativa L.) cultivation seasons and the new mid-size 2.7 m cutting width WORLD STAR WS7.0, 76 kW@2600 rpm combine harvester running on a total net area of 16.95 ha of plots for two rice cultivation seasons. The conventional combine as compared to mid-size combine showed 14.4% greater mean fuel consumptions (21.13 versus 18.46 l/ha), 31.1% greater mean effective field capacity (0.69 versus 0.53 ha/h), 5.23% greater cornering time (turning time) percentage of total time (8.28% versus 3.05%) and 1.41% greater reversing time percentage of total time (7.2% versus 5.79%) but 20.90% lesser mean operational speed (3.24 versus 4.10 km/h), 11.69% lesser effective time percentage of total time (60.0%versus 71.69%h/ha), 10.8% lesser mean field efficiency (64.3% versus 72.1%). In terms of total energy use the conventional combine showed 24.64% greater mean total energy use in the harvesting operation (1445.81 versus 1160.00 MJ/ha), 14.46% greater mean fuel energy (1010.014 versus 882.39 MJ/ha), 56.47% greater mean machinery energy (431.32 versus 275.65 MJ/ha) and 59.25% greater mean human energy (3.48 and 2.18 MJ/ha), this cause 26.12% greater mean total Green House Gas emission (GHG) than the mid-size combine. The results revealed that the mid-size combine is more suitable in conducting the harvest operation in rice field in Malaysia than the conventional combine.

In paddy cultivation, harvesting is the most important operation, which needs suitable machinery. Thus, this study was carried out to compare and evaluate field performances, grain quality and harvesting grain losses of conventional 5 m cutting width NEW HOLLAND CLAYSON 8080, combine running on a total net area of 42.78 hectares and the new midsize 2.7 m cutting width WORLD STAR WS7.0, combine running on a total net area of 16.95 hectares of plots for two rice (Oryza sativa L.) cultivation seasons. The conventional combine as compared to mid-size combine showed 14.4% greater mean fuel consumptions, 31.1% greater mean effective field capacity, 20.90% lesser mean operational speed and 10.8% lesser mean field efficiency. In terms of quality of harvested grain the conventional combine showed 9.48% lesser mean whole and healthy grain and 2.29 times greater mean broken grain and 85.78% greater mean foreign materials and 8.97 times greater mean empty grain than the mid-size combine. In terms of grain losses, the conventional combine showed 2.06 times greater mean total losses, 2.94 times greater mean cleaning losses, 1.03 times greater mean unthreshed losses than the mid-size combine. The results revealed that the mid-size combine is more suitable in conducting the harvesting operation in rice cultivation in Malaysia than the conventional combine.

As the machinery being operated in the field, the damage to the soil is obvious, which common damages appears mainly to the top soil layer such as increase in soil compaction and may create the ‘soft soil’ spot. However, the magnitude and the level of the damaged soil layer is challenging to quantify over the spatial distribution of the area. The study is focusing on comparing the level of soil compaction between rubber-wheeled tractor (RWT) and half-track tractor (HTT) during tillage operation using a rotovator implement in rice production. The widely used of HTT feared to cause damage to the soil hardpan as compared to common RWT type. A field study was conducted at Tunjang, Wilayah II – Jitra, Kedah with total area of 3.4 ha. More than 4000 data set of the soil pressured was measured using the soil penetrologger throughout two main growing seasons; wet and dry. The statistical analysis using T-test across all stages and operations revealed no significant different between the RTT and HTT on the soil compaction at the top soil layer (0-20 cm). However, the overall pressure from HTT is slightly higher than RWT, but below than threshold values of 1.4 MPa, thus may not limiting the root growth of the rice plant.

The heat treatment of oil palm fruit using saturated steam (413 K) in conventional oil palm processing has been reported to be ineffective in terms of heat distribution and penetration into the fruit bunch inner layer; hence, there is a desire to explore other alternative processes. In this study, oil palm fruit was treated in water at temperatures between 308 K and 343 K. The effects of the treatment on the in vivo activity of the lipase, the abscission layer of the fruit, and the integrity of the oil globule membrane were observed. The results showed in vivo residual lipase activity to be almost completely inactivated after 40 min of heat treatment at 343 K. The micrograph of the fruit mesocarp exhibited disintegration of the oil globule membrane as well as dissolution of the pectin layer architecture of the abscission zone after the treatment at this temperature. A dynamic mathematical modeling of heat transfer was employed, and coupled with reaction kinetics of lipase inactivation. The inactivation kinetics was found to be a non-elementary reaction, and the initial rate constant, k0dec, and activation energy, Edec, of the reaction were estimated to be 0.035 U−0.85/kg-mes−0.85⋅min and 153,052 kJ/kmol, respectively. The findings suggested the viability of water as a medium of heat treatment instead of the conventional steam treatment in oil palm processing.

Basal stem rot (BSR) is a prominent plant disease caused by Ganoderma boninense fungus, which infects oil palm plantations leading to large economic losses in palm oil production. There is need for novel disease detection techniques that can be used to reduce the oil palm losses due to BSR. Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz–30 MHz with 300 spectral interval. Genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS) were used to analyze the electrical properties of the dataset and the most significant frequencies were selected. Following the selection of significant frequencies, the features were evaluated using two classifiers, support vector machine (SVM) and artificial neural networks (ANN) to determine the overall and individual class classification accuracies. The selection model comparative feature analysis demonstrated that the best statistical indicators with overall accuracy (88.64%), kappa (0.8480) and low mean absolute error (0.1652) were obtained using significant frequencies produced by SVM-FS model. The results indicated that the SVM classifier shows better performance as compared to ANN classifier. The results also showed that the classes, features selection models, and the electrical properties were found to be significantly different (p < .1). The impedance values were highly classified by Ganoderma disease at different levels of severity with overall accuracies of more than 80%. Impedance can be considered as the best electrical properties that can be us

The potential application of near infrared (NIR) spectroscopy in the range of wavelength from 1000 to 2500 nm to non-destructively determine total soluble solids (Brix) and pH values of bananas were evaluated. Thirty banana samples were measured at five different maturity stages. Each banana sample was scanned at three different locations (top, middle and bottom). The Brix and pH values were associated with the absorbance spectral data for the model development which were split into prediction and calibration sets. The partial least squares (PLS) model was built based on both data sets of banana samples. The prediction model for the Brix values obtained a coefficient of determination of 0.81 and root means square error of predictions of 3.91 Brix. The prediction model for pH values had an R2 of 0.69 and RMSEP of 0.36 pH. These findings proposed that near infrared spectroscopy has great potential to predict sugar content in bananas.

Basal stem rot (BSR) is the most destructive disease in oil palm plantations caused by Ganoderma boninense fungus, leading to a major economic setback in palm oil production. In order to reduce the losses caused by this disease, an effective early detection method is needed. Early detection not only prevents production losses, but it also reduces the use of chemicals. Therefore, this paper aims at investigating an early detection method utilizing dielectric properties (impedance, capacitance, dielectric constant, and dissipation factor) of oil palm trees. Leaf samples of healthy, mild, moderate, and severely-infected trees were collected and leaves’ dielectric properties were measured at a frequency range of 100 kHz–30 MHz with 100 kHz intervals. These spectral data were then reduced by principal component analysis (PCA) method. Following that, the reduced spectral data were tested to classify the leaf samples into four levels of disease severity. The classifiers used are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), and Naïve Bayes (NB). The results showed that the dielectric spectra of oil palm leaves of diffident BSR severity levels were statistically different (p < 0.0004). In addition, despite the slight better performance of QDA classifier, ANOVA test revealed that there was no significant difference in accuracy between all other classifier models (p = 0.7169). Amongst the tested dielectric properties, impedance is considered the best parameter to assess the severity of BSR disease in oil palm with overall accuracy ranging from 81.82% to 100%. These results verify the potential of dielectric spectroscopy for detecting BSR disease in oil palm.

Plant diseases can greatly affect the total production of food and agricultural materials, which may lead to high amount of losses in terms of quality, quantity and also in economic sense. To reduce the losses due to plant diseases, early diseases detection either based on a visual inspection or laboratory tests are widely employed. However, these techniques are labor-intensive and time consuming. In a view to overcome the shortcoming of these conventional approaches, several researchers have developed non-invasive techniques. Recently, spectroscopy technique has become one of the most available non-invasive methods utilized in detecting plant diseases. However, most of the studies on the application of this novel technology are still in the experimental stages, and are carried out in isolation with no comprehensive information on the most suitable approach. This problem could affect the advancement and commercialization of spectroscopy technology in early plant disease detection. Here, we review the applications and limitations of spectroscopy techniques (visible/infrared, electrical impedance and fluorescence spectroscopy) in early detection of plant disease. Particular emphasis was given to different spectral level, challenges and future outlook.

Precision agriculture with regard to crop science was introduced to apply only the required and optimal amount of fertiliser, which inspired the present study of nutrient prediction for oil palm using spectroradiometer with wavelengths ranging from 350 to 2500 nm. Partial least square (PLS) method was used to develop a statistical model to interpret spectral data for nutrient deficiency of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and boron (B) of oil palm. Prior to the development of the PLS model, pre-processing was conducted to ensure only the smooth and best signals were studied, which includes the multiplicative scatter correction (MSC), first and second derivatives and standard normal variate (SNV), Gaussian filter and Savitzky-Golay smoothing. The MSC technique was the optimal overall pre-treatment method for nutrients in this study, with highest prediction R2 of 0.91 for N and lowest RMSEP value of 0.00 for P.

Basal stem rot (BSR) is a common plant disease that is largely responsible for high economic losses in oil palm production. Several novel techniques have recently been develop and reported in the literature for detecting BSR disease in oil palm plantations. However, studies on the application of electrical properties in detecting BSR disease in oil palm does not exist. Therefore, this paper aims to contribute to the existing knowledge by investigating the potential of dielectric constant (DC) and chlorophyll properties in detecting BSR disease in oil palms. The study involved the collection of different leaf samples namely; healthy, mild, moderate, and severely-infected. Impedance analyzer operating at a frequency range of 100 kHz-30 MHz with 300 spectral intervals and SPAD 502 were used to measure the DC and chlorophyll of the samples collected, respectively. ANOVA, Duncan's multiple range test (DMRT) and principal component analysis (PCA) were used for statistical analysis. The results of this study showed a significant relationship between DC and different severity levels of BSR disease (p <; 0.0001). Specifically, BSR disease severity levels of all samples collected were clearly discriminated based on DC. Conversely, the chlorophyll content could not classify the different levels of BSR disease into distinct separate groups but two groups (healthy and BSR-infected). As such, the results demonstrated that DC and chlorophyll content at certain extend could be used as a sensing parameter for Ganoderma disease detection.

The color changes kinetics of pumpkin slices during convective hot air drying was investigated at drying temperatures of 50, 60, 70 and80°C. The hunter lab L* a* and b* color coordinates were used as assessment indicators. The total color change ∆E, Chroma value, hue angle and brownness index (BI) of the pumpkin slices where also determined. To determine the most suitable kinetics model for the prediction of the color changes of pumpkin, the zero-order, first-order, and fractional conversion models were fitted to the experimental data, using linear regression analysis. The activation energy of the color change parameters (L*, a*, b* and ∆E) was estimated and found to be 41.59, 16.287 63.856 and73.390 kJ/mol respectively. The fresh pumpkin samples contained a mean total carotenoid content of 25 "-g/g, while the total carotenoid content of samples dried at 50, 60, 70 and 80°C were 146, 56.4, 37.9 and 102.5 "-g/g, respectively. Further, the results of ANOVA showed there was significant difference between the total carotenoid content of the fresh pumpkin samples and those dried by convective hot air dryer at 5% (p<0.05) significant level.

Oil palm crop has been an important source of income to Malaysian economy, thus it is important to ensure the crops obtain optimum nutrient supply to achieve a higher productivity. This study aimed to investigate the ability of near-infrared reflectance spectroscopy for predicting nutrient deficiency of oil palm tree based on its leaf samples. Near-infrared spectral data was measured using a full range spectroradiometer with wavelength ranging from 350 to 2500 nm from three different frond numbers, namely frond 3, frond 9 and frond 17. Partial least square method was used to develop calibration and prediction models data for the prediction of nitrogen, phosphorus and potassium of oil palm. The result indicated that the full range spectrometer can be used to predict the nutrient deficiency of oil palm tree based on 30 leaf samples. Frond 17 was found to have a better prediction accuracy than frond 3 and frond 9. The value of coefficient of determination (R2) for frond 17 for values of nitrogen, phosphorus and potassium of 0.98, 0.98 and 0.98 while frond 3 results with 0.21, 0.12 and 0.19 and frond 9 had values of 0.05, 0.49 and 0.48 respectively. In terms of Root Mean Square Error of Prediction for frond 17 ranged between 1.40 and 1.55 while frond 3 and frond 9 ranges from 0.01 to 0.15 and 0.01 to 0.21 respectively. In summary, spectroradiometer can be used to predict nutrient deficiency in oil palm frond frond17 using partial least square analysis.

Soluble solid content (SSC) is one of the important traits that indicate the ripeness of banana fruits. Determination of SSC for banana often requires destructive laboratory analysis on the fruit. An impedance measurement technique was investigated as a non-destructive approach for SSC determination of bananas. A pair of electrocardiogram (ECG) electrode connected to an impedance analyser board was used to measure the impedance value of bananas over the frequency of 19.5 to 20.5 KHz. The SSC measurement was conducted using a pocket refractometer and data was analysed to correlate SSC with impedance values. It was found that the mean of impedance, Z decreased from 10.01 to 99.93 KO at the frequency of 20 KHz, while the mean value of SSC increased from 0.58 to 4.93 % Brix from day 1 to day 8. The best correlation between impedance and SSC was found at 20 KHz, with the coefficient of determination, R2 of 0.87. This result indicates the potential of impedance measurement in predicting SSC of banana fruits.

Pesticides have long been used in cabbage industry to control pests. This study aimed to investigate the potential application of visible shortwave near infrared spectroscopy for detection of typical pesticide (deltamethrin) on round cabbages. In this study, a total of 60 round cabbages were used. The sample were divided into four batches. Three batches of round cabbage were sprayed with deltamethrin at three different concentrations level of pesticides namely low, medium and high with values of 0.08, 0.11 and 0.14 % (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near infrared spectrometer (VSWNIRS) with the wavelength range between 200 to 1100 nm. The spectral data was pretreated using multiple scattering correction (MSC) method in order to obtain optimal prediction values. Gas chromatography was used to determine the multi-residue limit (MRL) value of the samples. Calibration and prediction models were developed to correlate the spectral data with MRL values using partial least square regression (PLS) method. The calibration model produced the values of coefficient of determination (R2) and root mean square errors (RMSEP) of 0.98 and 0.02, respectively. The prediction models gave good R2 and RMSEC values of 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement provide a promising technique for pesticide detection at different level of concentration on round cabbage.

The drying of fruits and vegetables is a complex operation that demands much energy and time. In practice, the drying of fruits and vegetables increases product shelf-life and reduces the bulk and weight of the product, thus simplifying transport. Occasionally, drying may lead to a great decrease in the volume of the product, leading to a decrease in storage space requirements. Studies have shown that dependence purely on experimental drying practices, without mathematical considerations of the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product. Thus, the use of mathematical models in estimating the drying kinetics, the behavior, and the energy needed in the drying of agricultural and food products becomes indispensable. This paper presents a comprehensive review of modeling thin-layer drying of fruits and vegetables with particular focus on thin-layer theories, models, and applications since the year 2005. The thin-layer drying behavior of fruits and vegetables is also highlighted. The most frequently used of the newly developed mathematical models for thin-layer drying of fruits and vegetables in the last 10 years are shown. Subsequently, the equations and various conditions used in the estimation of the effective moisture diffusivity, shrinkage effects, and minimum energy requirement are displayed. The authors hope that this review will be of use for future research in terms of modeling, analysis, design, and the optimization of the drying process of fruits and vegetables.

This study seeks to investigate the effects of temperature (50, 60, 70 and 80 °C) and material thickness (3, 5 and 7 mm), on the drying characteristics of pumpkin (Cucurbita moschata). Experimental data were used to estimate the effective moisture diffusivities and activation energy of pumpkin by using solutions of Fick’s second law of diffusion or its simplified form. The calculated value of moisture diffusivity with and without shrinkage effect varied from a minimum of 1.942 × 10–8 m2/s to a maximum of 9.196 × 10–8 m2/s, while that of activation energy varied from 5.02158 to 32.14542 kJ/mol with temperature ranging from 50 to 80 °C and slice thickness of 3 to 7 mm at constant air velocity of 1.16 m/s, respectively. The results indicated that with increasing temperature, and reduction of slice thickness, the drying time was reduced by more than 30 %. The effective moisture diffusivity increased with an increase in drying temperature with or without shrinkage effect. An increase in the activation energy was observed due to an increase in the slice thickness of the pumpkin samples.