AgriEngineering, Volume 2, Issue 4 (December 2020) – 11 articles

This study examines sustainable agricultural development in Vietnam by focusing on rice production in the Mekong Delta. Vietnam is the third largest rice-exporting country in the world and more than 90% of rice for export is from the Mekong Delta. We attempt to identify changes in the rice industry, specifically examining farming mechanization, trends in farm operation, and farm household economy. The main structure of our study is based on our direct interviews with 420 farmer households, 81 registered large farm owners, 75 farming service providers, and local government members. We carried out those interviews in 2013 and 2014. We identified several important changes brought by technological advances, economic modernization, government policies, and environmental movements. One important finding was the increasing trend of agricultural land per farmer household, such as from 1.98 to 2.27 ha in 2010 to 2018, and the decreasing trend of labor force, which pushed up agricultural mechanization. Another point was the land law revised in 2013, which allowed farmers to borrow (no private land ownership in Vietnam) farms up to 30ha/household if registered as a “Trang Trai” farm. Farmers accepted the use of tractors and combine harvesters; however, rice seed sowing was not mechanized, so rice transplanters were rarely used for rice planting. Among the processes of rice production, sowing of rice seeds was the least mechanized. In order to achieve more sustainable agricultural practices, we recommend improving the mechanization of the rice seed sowing process for the Mekong Delta farmers with acceptable conditions. Another finding was positive movements among Mekong Delta researchers and farmers to improve their rice quality to be accepted as high-quality rice in the global rice market and to dispel the bad reputation of the past. Full article

Rollers/crimpers have been used to terminate cover crops typically with supplemental herbicide application to speed-up termination. Due to environmental concerns, there is a need to reduce herbicide use. In the Southern USA, the cash crop is typically planted three weeks after a rolled cover crop reaches more than 90% kill rate which eliminates competition with the cash crop for water and nutrients. A three-year replicated field experiment was initiated in the fall of 2014 to determine the effects of recurring rolling by experimental rollers/crimpers in terminating a cereal rye cover crop in central Alabama and how multiple rolling affected soil strength. Experimental 2-stage (one smooth drum and one crimping drum), 4-stage (one smooth drum and three crimping drums), spiral, and smooth rollers were tested to roll rye 1, 2, and 3 times (conducted at the same day). A smooth roller with mounted spray boom applying glyphosate rolling once was also evaluated, and untreated rye was the control. Rye was terminated at the milk growth stage and was evaluated one, two and three weeks after rolling. At one week after rolling, the highest rye kill rate was obtained with rolling three times by the 4-stage (96%), 2-stage (92%), spiral roller (81%); rolling once by the smooth roller with glyphosate (94%) compared to the untreated rye at 37%. At two weeks after rolling no differences among rollers were found (91% to 98%); the untreated rye was 54%. Similarly, at three weeks after rolling no differences were detected among rollers (99% to 100%); the untreated rye was 86%. Rolling 2 or 3 times compared with rolling one time did not cause soil compaction. However, at the 15 cm depth cone index (CI) did exceed 2 MPa (a critical value of root penetration restriction); although this CI increase was solely related to decrease in gravimetric soil moisture content (GMC). In addition, over three growing seasons, the seed cotton yield was not affected by rolling treatments with the average yield of 3601 kg ha⁻¹ (1512 kg ha⁻¹ of cotton lint). Full article

A biorefinery purchasing feedstock (perennial grass) must offer contracts that provide the same opportunity to earn a profit for a feedstock contractor located 50 or 5 km from the biorefinery. The business plan presented here specifies that the biomass is purchased in satellite storage locations (SSLs), and the load-out and hauling costs are paid by the biorefinery. Contracts can be offered for harvest in September, October, and November, a three-month harvest window, or the harvest window can be extended to December, January, and February, a six-month harvest window. Required total storage capacity is 75% of annual consumption for the three-month window and 50% for the six-month window, a significant difference in total storage capacity (cost). The storage cost difference paid by the biorefinery is 5.27 and 3.52 USD/Mg for the three-month and six-month, respectively. Several issues must be addressed in the feedstock contracts: (1) earlier harvest, before plant senescence, means less nutrients are translocated back into the soil and more are removed at harvest; (2) harvest losses are higher for all harvests after the September harvest; and (3) storage losses increase with storage time in the SSL. Time of removal from the SSL is dictated by the biorefinery; thus, the feedstock contractor must be compensated. The contracts paid by the biorefinery, averaged across the entire annual consumption, were about the same for the three-month window, and six-month window. This result was obtained because fertilizer cost decreases and harvest losses increase as the harvest date increases; thus, the two factors tend to offset. Using a 77 USD/Mg base cost, representative feedstock payment at the SSL (no storage losses included) for contractors with various month contracts are September (84.30), October (85.54), November (86.72), December 88.63), January (89.98), and February (90.58). Subsequent compensation for storage losses depends on the amount of time the particular unit of biomass is in storage before shipment. Full article

Sorting is one of the most critical factors in the marketing development of fruit and vegetable and should be performed without any damage to the product. This article reports results of the development and testing of a prototype of a low-cost mechanical spherical fruit sorter based on a belt-and-roller device built at the State University of Tabriz, Iran. The efficiency and damage effect of the prototype of the machine was tested at different sorting rates on apples (Red Delicious and Golden Delicious) and oranges. Performance tests indicated that the speed of the feeding belt and transporting belt as well as the spherical coefficient significantly affect the machine’s sizing performance and damages. The results of the test showed a 95.28% and 92.48% accuracy in sorting for Red Delicious and Golden Delicious, respectively, and 94.28% for orange. Furthermore, the machine sorts fruits without any significant damage. Full article

The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before an outbreak occurs, it is often manually assessed and therefore labor intense, cost intensive and impracticable on a commercial scale. Recent advances of semi- and unsupervised convolutional neural networks (CNNs) have made them to the state of art technology for detecting anomalous behavior patterns in a variety of complex scene environments. In this study we apply such a CNN for anomaly detection to identify varying levels of activity in a multi-pen problem setup. By applying a two-stage approach we first trained the CNN to detect anomalies in the form of extreme activity behavior. Second, we trained a classifier to categorize the detected anomaly scores by learning the potential activity range of each pen. We evaluated our framework by analyzing 82 manually rated videos and achieved a success rate of 91%. Furthermore, we compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set. The results show the effectiveness of our framework, which can be applied without the need of a labor intense manual annotation process and can be utilized for the assessment of the pig activity in a variety of applications like early warning systems to detect changes in the state of health. Full article

Herbaceous biomass will contribute significantly to meeting renewable energy goals. Harvesting equipment for hay is generally suitable for mowing, raking, and baling grasses such as switchgrass; however, there is a need for field data to better understand machine performance in energy crops. The purpose of this study was to collect field data to estimate baler field capacity, throughput, and speed. Data gathered with a Differential Global Positioning System (DGPS) unit during baling provided time-motion studies of baler productivity. Six fields were used to compare field capacity, speed, and throughput results from four round balers and one large-square baler. The results show that in-field performance of round balers is significantly affected by yield, but that the relationship can be represented with machinery management concepts, knowledge of maximum throughput, and wrap-eject time. Baler performance will be overestimated if the yield, maximum throughput, and wrap-eject time are not correctly accounted for. Full article

Black spot is one of the seriously damaging plant diseases in China, especially in rose production. Hyperspectral technology reflects both external features and internal structure information of measured samples, which can be used to identify the disease. In this research, both the spectral and image features of two infected roses with black spot were used to train a convolutional neural network (CNN) model. Multiple scattering correction (MSC) and standard normal variable (SNV) methods were applied to preprocess the spectral data. Cropping, median filtering and binarization were pretreatments used on the hyperspectral images. Three CNN models based on Alexnet, VGG16 and neural discriminative dimensionality reduction (NDDR) were evaluated by analyzing the classification accuracy and loss function. The results show that the CNN model based on the fusion of features has higher accuracy. The highest accuracies of detection of blackspot in different roses are 12–26 (100%) and 13–54 (99.95%), applying the NDDR-CNN model. Therefore, this research indicates that the spectral analysis based on CNN can detect black spot of roses, which provides a reference for the detection of other plant diseases, and has favorable research significance as well as prospect for development. Full article

The performance of a mixture between L. sphaericus and E. fetida was evaluated for ammonium cycling and nitrifying bacteria stimulation during oil sludge remediation. The addition of E. fetida significantly increased ammonium concentration (p = 0.0218) and total Colony-forming units (CFU) count p = 0.02848). However, oil sludge with worms and L. sphaericus reached lower ammonium concentrations and CFU counts than sludge with worms alone. Sludge inoculated only with L. sphaericus presented higher ammonium concentration than sludge without inoculum, but the bacterial population reached a lower density during the final days. Final DNA and RNA extractions from all treatments amplified for L. sphaericus putative amoA and Gram-negative nitrifying bacteria amoA genes correlated with diminished ammonium concentrations during the final days of the experiment. Final RNA extractions for L. sphaericus amplified for Molybdenum transporter gene suggesting possible nitrogen fixation by L. sphaericus. The addition of Red Wiggler Earthworm to oil sludge remediation systems may provide better conditions for bacterial populations to carry out hydrocarbon degradation. The addition of E. fetida to a L. sphaericus crude oil biodegradation system may improve soil ammonium concentrations and nitrifying activity, and this could be crucial in oil sludge remediation because of bacterial inhibition due to high C:N ratios. The final product of this process may be used for soil enhancement due to its richness in nutrients and beneficial bacterial populations. Full article

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients ($R^2$) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha⁻¹ in grasslands and 1.3 t ha⁻¹ in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R² = 0.9 and CP R² = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring. Full article

In many special agricultural environments, many wireless sensors have a problem of power supply selection. Energy harvesting in the agricultural environment based on vortex-induced vibration (VIV) has the potential to solve the problem. In this paper, an energy harvester based on the VIV is designed in an agricultural environment. Relevant parameters of the harvester are studied with wind tunnel experiment to improve the efficiency of energy conversation. The results show that: (i) For large mass ratio, $m^{*}\gg 1$, and the same mass ratio $m^{*}$, the smaller the damping ratio $\zeta$, the larger the normalized amplitude $A^{*}$, the larger the maximum efficiency $\eta$ of VIV energy harvesting; (ii) $m^{*}\gg 1$, and under a certain range of Reynolds numbers, the smaller the mass-damping parameter $m^{*}\zeta$, the larger the normalized amplitude $A^{*}$, the larger the maximum and average efficiency $\eta$ of VIV energy harvesting. (iii) $m^{*}\gg 1$, the larger the mass ratio $m^{*}$, the larger the range of resonance; the normalized frequency $f^{*}\simeq 1$, the stable VIV locked state appears. The research results can provide references for the design of VIV energy harvesters in agricultural environments. Full article

Reasonable use of agricultural machinery has an extraordinary potential for poverty alleviation by increasing land and labor productivity in Thailand, Vietnam, and even in Bangladesh. This study was conducted under a program entitled “Agriculture Mechanization, Agro-Processing, Value addition and Export Market Development in Thailand and Vietnam from 1–14 November, 20I9” from the Ministry of Agriculture, Bangladesh. In all three distinct nations, farming activities represent a significant area of activity and remains the biggest wellspring of agricultural business. About 10.5% of Thailand’s, 21.5% of Vietnam’s, and 14.23% of Bangladesh’s GDP come from agriculture. For sustainable development, it is essential to modernize agriculture through the mechanization of its operations, which is therefore inevitable in the studied countries. Thailand’s government started mechanization in 1891 with the import of steam-powered tractor and rotary hoes. Since then the country has witnessed several milestones in the course of mechanization development. The focal plain agro-ecological zone of the state is the maximum and almost fully modernized area. As of now, there are two methods of practicing farming apparatus use: as a proprietor and/or through custom renting provision which coincides with Vietnam and Bangladesh. Historically, mechanization patterns in Vietnam can been described by tillage machinery with associated implement equipment use preceding 1975. This was non-linear, followed by a decreasing trend during the 80s prior to recovery during the 90s, with significant disparities in implementation status across the areas. In 2018, the number of tillage implements and harvesters was boosted about 1.6 and 25.6 times, respectively compared with 2006. The percentage of machinery use in soil tillage operation is 80% of the whole territory of cultivable land in Vietnam, compared to about 90% in Bangladesh and 100% in Thailand. Mechanization in Bangladesh started before independence with the importation of 2-wheel tractors and irrigation pumps in the last part of the 1960s as part of ‘Green Revolution’ activities. To continue this momentum, the Bangladesh Government permitted the continuation of agricultural machinery importation after later autonomy. Machinery use in different agricultural activities has increased in recent years in the areas of irrigation, land preparation, intercultural operation, and threshing. Though its degree of advancement is by and large still quite low contrasted with other South Asian nations, it is noticeable that the most recent two decades, the pace of mechanization has increased rapidly with the increase of mechanical power use in farm activities. The use of farm machinery in rice cultivation has been the most amazing when contrasted with different crops in these three nations. A clear comparison has been given in the paper, which aims to help researchers and policymakers take necessary measures. Full article