Journal Description
AgriEngineering
AgriEngineering
is an international, peer-reviewed, open access journal on the engineering science of agricultural and horticultural production, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), PubAg, FSTA, AGRIS, CAPlus / SciFinder, and other databases.
- Journal Rank: CiteScore - Q1 (Horticulture)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 25.8 days after submission; acceptance to publication is undertaken in 5.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.8 (2022);
5-Year Impact Factor:
2.7 (2022)
Latest Articles
Post-Harvest Management of Immature (Green and Semi-Green) Soybeans: Effect of Drying and Storage Conditions (Temperature, Light, and Aeration) on Color and Oil Quality
AgriEngineering 2024, 6(1), 135-154; https://doi.org/10.3390/agriengineering6010009 - 15 Jan 2024
Abstract
Soybean downgrading due to immature (green and semi-green) color at harvest, caused by frost conditions, poses a significant loss to producers and processors. After harvest, drying and storage are important for preserving the quality of the harvested produce. This study investigated the impact
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Soybean downgrading due to immature (green and semi-green) color at harvest, caused by frost conditions, poses a significant loss to producers and processors. After harvest, drying and storage are important for preserving the quality of the harvested produce. This study investigated the impact of drying on color change in harvested immature soybeans and the effect of the soybean moisture content, storage environment (temperature, light, and aeration), and storage period on color change and oil quality of immature soybeans. Soybeans were harvested at three different maturity stages: R6 (green) and R7 (semi-green) in pods and R8 (fully matured) in seed. The soybeans in pods were dried, shelled, and conditioned to moisture contents of 12% and 17% (wet basis) prior to storage in 12 storage chamber (box) environments. The chambers were built to have four environments of “light” and “no light” with and without aeration and were stored at temperatures of either 4 °C or 23.5 °C for 24 weeks. Samples were taken every 2 weeks for 2 months and then bimonthly in storage. Soybean color change during drying and their chlorophyll, color, peroxide value (PV), and free fatty acid (FFA) status in storage were determined. Visual observation showed that R6 (green) soybean color faded after 48 h drying, which was supported with a colorimeter reading as the “a” value increased from −8.89 to −3.83 and −8.89 to −1.71 with 37 °C and 27 °C drying temperatures, respectively. The ANOVA analysis showed that light had the greatest contribution (~81%) to the color change compared to the other three storage environment factors of temperature (~9.1%), aeration (~8%), and moisture content (~1.5%) with <10% separate effects. During storage, the R6 green and R7 semi-green soybean color continued to fade with color a-values that exceeded the initial values of the R8 matured (control) by 353% and 350%, respectively, by the end of the storage period. Low amounts of peroxide and free fatty acids (FFA) were recorded throughout the storage period. Only the FFA of 17% M.C. soybeans stored at 23.5 °C exceeded acceptable limits at the end of the storage period. Exposing immature (green and semi-green) soybeans to light resulted in the fading of the green color. Seed producers in regions prone to frost can extend harvest time by allowing immature soybeans to field-dry.
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(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications)
Open AccessArticle
A Study of an Agricultural Indoor Robot for Harvesting Edible Bird Nests in Vietnam
AgriEngineering 2024, 6(1), 113-134; https://doi.org/10.3390/agriengineering6010008 - 12 Jan 2024
Abstract
This study demonstrates robot technology for harvesting edible bird’s nests within swiftlet houses. A comprehensive manipulator's movement analysis of harvesting operation with a separating tool is provided for precisely collecting swiftlet nests. A robotic manipulator mounted on a mobile platform with a vision
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This study demonstrates robot technology for harvesting edible bird’s nests within swiftlet houses. A comprehensive manipulator's movement analysis of harvesting operation with a separating tool is provided for precisely collecting swiftlet nests. A robotic manipulator mounted on a mobile platform with a vision system is also analyzed and evaluated in this study. The actual harvesting or separating the swiftlet nests is performed with visual servo feedback. The manipulator performs the gross motions of separating tools and removing the nests under computer control with velocity and position feedback. The separating principle between the objective nest and wooden frame has been applied to a demonstration removal of nests using a four-degrees-of-freedom manipulator to perform the gross movements of tool. The actual separations using this system are accomplished as fast as the manipulator can be controlled to perform the necessary deceleration and topping at the end of separating. This is typically 2.0 s. This efficiency underscores the system’s capability for swift and precise operation in harvesting an edible bird nest task.
Full article
(This article belongs to the Special Issue Innovations in Agricultural Engineering and Mechanization for Sustainable Agriculture, Forestry and Food Production)
Open AccessArticle
A Novel Algorithm to Detect White Flowering Honey Trees in Mixed Forest Ecosystems Using UAV-Based RGB Imaging
AgriEngineering 2024, 6(1), 95-112; https://doi.org/10.3390/agriengineering6010007 - 11 Jan 2024
Abstract
Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study
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Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary.
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(This article belongs to the Special Issue Computer Vision for Agriculture and Smart Farming)
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Open AccessArticle
Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data
by
, , and
AgriEngineering 2024, 6(1), 81-94; https://doi.org/10.3390/agriengineering6010006 - 10 Jan 2024
Abstract
Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and
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Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and also continuous production of coffee over time. Big Data retrieved from remote sensing can be tested to improve spatial modeling. This research proposes to apply the Sentinel-2 vegetation index (NDVI) and the Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) dataset as auxiliary variables in the multivariate geostatistical modeling of coffee yield characterized by the presence of outliers and assess improvement. A total of 66 coffee yield points were sampled from a 4 ha area in a quasi-regular grid located in southeastern Brazil. Ordinary kriging (OK) and block cokriging (BCOK) were applied. Overall, coupling coffee yield with the NDVI and/or SAR in BCOK interpolation improved the accuracy of spatial interpolation of coffee yield even in the presence of outliers. Incorporating Big Data for improving the modeling for low-sampled fields requires taking into account the difference in supports between different datasets since this difference can increase uncontrolled uncertainty. In this manner, we will consider, for future research, new tests with other covariates. This research has the potential to support precision agriculture applications as site-specific plant nutrient management.
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(This article belongs to the Special Issue Big Data Analytics in Agriculture)
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Open AccessArticle
Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy?
AgriEngineering 2024, 6(1), 64-80; https://doi.org/10.3390/agriengineering6010005 - 09 Jan 2024
Abstract
In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation.
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In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers.
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(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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Open AccessArticle
Determination of Dry-Matter Content of Kiwifruit before Harvest Based on Hyperspectral Imaging
AgriEngineering 2024, 6(1), 52-63; https://doi.org/10.3390/agriengineering6010004 - 08 Jan 2024
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Determining pre-harvest fruit maturity is vital to ensure the quality of kiwifruit, and dry-matter content is an important indicator of kiwifruit ripeness. To predict the pre-harvest dry-matter content of kiwifruit continuously in real-time with high accuracy, this study uses hyperspectral data of pre-harvest
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Determining pre-harvest fruit maturity is vital to ensure the quality of kiwifruit, and dry-matter content is an important indicator of kiwifruit ripeness. To predict the pre-harvest dry-matter content of kiwifruit continuously in real-time with high accuracy, this study uses hyperspectral data of pre-harvest Jintao kiwifruit obtained by using a hyperspectral image acquisition device. The raw data underwent whiteboard correction, spectral data extraction, spectral pre-processing, and feature-band extraction, following which the dry-matter content of the fruit was predicted by using partial least squares (PLS) regression. The feature bands extracted by the random frog method were 538.93, 671.14, 693.41, 770.61, 796.98, 813.24, 841.21, 843.29, and 856.80 nm, which improve the accuracy of the PLS method for predicting dry-matter content, with R2 = 0.92 and a root mean square error (RMSE) of 0.41% for the training set, and R2 = 0.85 and a RMSE of 0.50% for the test set. These results show that the proposed method reduces the number of required bands while maintaining the prediction accuracy, thereby demonstrating the reliability of using hyperspectral data to predict the pre-harvest dry-matter content of kiwifruit. This method can effectively guide the management of kiwifruit harvesting period, establishing a theoretical foundation for precise unmanned harvesting.
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Open AccessArticle
The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China
AgriEngineering 2024, 6(1), 34-51; https://doi.org/10.3390/agriengineering6010003 - 08 Jan 2024
Abstract
Promoting the mechanization of aquaculture is one of the most important supporting measures to ensure the high-quality development of the aquaculture industry in China. In order to solve the problems of predominantly manual work and to decrease the costs of aquaculture, the influencing
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Promoting the mechanization of aquaculture is one of the most important supporting measures to ensure the high-quality development of the aquaculture industry in China. In order to solve the problems of predominantly manual work and to decrease the costs of aquaculture, the influencing factors of China’s aquaculture mechanization were systematically analyzed. The triple bottom theory was selected, and three aspects were identified, including environmental, economic, and social aspects. Through the literature review, the Delphi method, and the analytic hierarchy process, the comprehensive evaluation indicator system, including 18 influencing factors, was proposed. Moreover, the fuzzy comprehensive evaluation method was combined with the model to solve the evaluation results. A case study in Liaoning Province was offered and, according to the analysis results, the economic aspect at the first level was the most critical factor; the financial subsidy for the purchase of aquaculture machinery, the energy consumption of the machinery and equipment, and the promotion and use of aquaculture technology were the most important factors and had the greatest impact on the development of aquaculture mechanization in China. The effective implementation paths and countermeasures were proposed, such as the promotion of mechanized equipment and the enhancement of the machinery purchase subsidies, in order to provide an important decision-making basis for the improvement of the level of aquaculture mechanization.
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(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications)
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Open AccessArticle
Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning
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, , , and
AgriEngineering 2024, 6(1), 20-33; https://doi.org/10.3390/agriengineering6010002 - 05 Jan 2024
Abstract
The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied
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The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation.
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(This article belongs to the Special Issue Remote Sensing-Based Machine Learning Applications in Agriculture)
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Open AccessTechnical Note
Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Hand Intrusion Sensor Design
AgriEngineering 2024, 6(1), 1-19; https://doi.org/10.3390/agriengineering6010001 - 22 Dec 2023
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Plastic contamination in cotton lint poses significant challenges to the U.S. cotton industry, with plastic wrap from John Deere round module harvesters being a primary contaminant. Despite efforts to manually remove this plastic during module unwrapping, some inevitably enters the cotton gin’s processing
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Plastic contamination in cotton lint poses significant challenges to the U.S. cotton industry, with plastic wrap from John Deere round module harvesters being a primary contaminant. Despite efforts to manually remove this plastic during module unwrapping, some inevitably enters the cotton gin’s processing system. To address this, a machine-vision detection and removal system has been developed. This system uses inexpensive color cameras to identify plastic on the gin stand feeder apron, triggering a mechanism that expels the plastic from the cotton stream. However, the system, composed of 30–50 Linux-based ARM computers, requires substantial effort for calibration and tuning and presents a technological barrier for typical cotton gin workers. This research aims to transition the system to a more user-friendly, plug-and-play model by implementing an auto-calibration function. The proposed function dynamically tracks cotton colors while excluding plastic images that could hinder performance. A critical component of this auto-calibration algorithm is the hand intrusion detector, or “HID”, which is discussed in this paper. In the normal operation of a cotton gin, the gin personnel periodically have to clear the machine, which entails running a stick or their arm/hand under the detection cameras. This results in the system capturing a false positive, which interferes with the ability of auto-calibration algorithms to function correctly. Hence, there is a critical need for an HID to remove these false positives from the record. The anticipated benefits of the auto-calibration function include reduced setup and maintenance overhead, less reliance on skilled personnel, and enhanced adoption of the plastic removal system within the cotton ginning industry.
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Open AccessArticle
Coffee Growing with Remotely Piloted Aircraft System: Bibliometric Review
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, , and
AgriEngineering 2023, 5(4), 2458-2477; https://doi.org/10.3390/agriengineering5040151 - 15 Dec 2023
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Remotely piloted aircraft systems (RPASs) have gained prominence in recent decades primarily due to their versatility of application in various sectors of the economy. In the agricultural sector, they stand out for optimizing processes, contributing to improved sampling, measurements, and operational efficiency, ultimately
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Remotely piloted aircraft systems (RPASs) have gained prominence in recent decades primarily due to their versatility of application in various sectors of the economy. In the agricultural sector, they stand out for optimizing processes, contributing to improved sampling, measurements, and operational efficiency, ultimately leading to increased profitability in crop production. This technology is becoming a reality in coffee farming, an essential commodity in the global economic balance, mainly due to academic attention and applicability. This study presents a bibliometric analysis focused on using RPASs in coffee farming to structure the existing academic literature and reveal trends and insights into the research topic. For this purpose, searches were conducted over the last 20 years (2002 to 2022) in the Web of Science and Scopus scientific databases. Subsequently, bibliometric analysis was applied using Biblioshiny for Bibliometrix software in R (version 2022.07.1), with emphasis on the temporal evolution of research on the topic, performance analysis highlighting key publications, journals, researchers, institutions, countries, and the scientific mapping of co-authorship, keywords, and future trends/possibilities. The results revealed 42 publications on the topic, with the pioneering studies being the most cited. Brazilian researchers and institutions (Federal University of Lavras) have a strong presence in publications on the subject and in journals focusing on technological applications. As future trends and possibilities, the employment of technology optimizes the productivity and profitability studies of coffee farming for the timely and efficient application of aerial imaging.
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Open AccessArticle
Harnessing Solar Energy: A Novel Hybrid Solar Dryer for Efficient Fish Waste Processing
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, , , , and
AgriEngineering 2023, 5(4), 2439-2457; https://doi.org/10.3390/agriengineering5040150 - 15 Dec 2023
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Facing severe climate change, preserving the environment, and promoting sustainable development necessitate innovative global solutions such as waste recycling, extracting value-added by-products, and transitioning from traditional to renewable energy sources. Accordingly, this study aims to repurpose fish waste into valuable, nutritionally rich products
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Facing severe climate change, preserving the environment, and promoting sustainable development necessitate innovative global solutions such as waste recycling, extracting value-added by-products, and transitioning from traditional to renewable energy sources. Accordingly, this study aims to repurpose fish waste into valuable, nutritionally rich products and extract essential chemical compounds such as proteins and oils using a newly developed hybrid solar dryer (HSD). This proposed HSD aims to produce thermal energy for drying fish waste through the combined use of solar collectors and solar panels. The HSD, primarily composed of a solar collector, drying chamber, auxiliary heating system, solar panels, battery, pump, heating tank, control panel, and charging unit, has been designed for the effective drying of fish waste. We subjected the fish waste samples to controlled drying at three distinct temperatures: 45, 50, and 55 °C. The results indicated a reduction in moisture content from 75.2% to 24.8% within drying times of 10, 7, and 5 h, respectively, at these temperatures. Moreover, maximum drying rates of 1.10, 1.22, and 1.41 kgH2O/kg dry material/h were recorded at 45, 50, and 55 °C, respectively. Remarkable energy efficiency was also observed in the HSD’s operation, with savings of 79.2%, 75.8%, and 62.2% at each respective temperature. Notably, with an increase in drying temperature, the microbial load, crude lipid, and moisture content decreased, while the crude protein and ash content increased. The outcomes of this study indicate that the practical, solar-powered HSD can recycle fish waste, enhance its value, and reduce the carbon footprint of processing operations. This sustainable approach, underpinned by renewable energy, offers significant environmental preservation and a reduction in fossil fuel reliance for industrial operations.
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Open AccessArticle
Dynamic Behavior Forecast of an Experimental Indirect Solar Dryer Using an Artificial Neural Network
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, , , , and
AgriEngineering 2023, 5(4), 2423-2438; https://doi.org/10.3390/agriengineering5040149 - 14 Dec 2023
Abstract
This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model
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This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes.
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(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)
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Open AccessArticle
Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions
AgriEngineering 2023, 5(4), 2408-2422; https://doi.org/10.3390/agriengineering5040148 - 12 Dec 2023
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Climate change is linked to CO2 emissions, the reduction of which has become a top priority. In response to these circumstances, scientists must constantly develop new technologies that increase fuel efficiency and reduce emissions. Agriculture today is dominated by arable fields of
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Climate change is linked to CO2 emissions, the reduction of which has become a top priority. In response to these circumstances, scientists must constantly develop new technologies that increase fuel efficiency and reduce emissions. Agriculture today is dominated by arable fields of various sizes, shapes, and dimensions, and to achieve fuel economy and environmental impact requirements, it is not enough to know only the principles of optimization of tillage processes; it is also necessary to understand the influence of field size and its shape and dimensions on tillage performance. The purpose of this research is to present a methodology that allows predicting tractor fuel demand and CO2 emissions per unit of ploughed area when ploughing field plots with different shapes and dimensions and to confirm a suitable variable for such a prediction. Theoretical calculations and experimental tests have shown that the field ploughing time efficiency coefficient is a useful metric for comparing field plots of different shapes and dimensions. This coefficient effectively describes tractor fuel consumption and CO2 emissions during ploughing operations on differently configured field plots. A reasonable method for calculating the real field ploughing time efficiency coefficient is based on field and tillage data and a practical determination method using tractor engine load reports. It was found that during the research, when ploughing six field plots of different shapes and dimensions, with an area of 6 ha, the field ploughing time efficiency coefficient varied from 0.68 to 0.82, and fuel consumption between 15.6 and 16.5 kg/ha. In the field plot of 6 ha, where the field ploughing time efficiency coefficient was 15% higher, the fuel consumption per unit area was lower by about 5.5%. The results of this study will help to effectively predict tillage time and tractor fuel consumption required for different field shapes and dimensions.
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Open AccessArticle
Coconut Mesocarp Extracts to Control Fusarium musae, the Causal Agent of Banana Fruit and Crown Rot
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, , , , , , and
AgriEngineering 2023, 5(4), 2395-2407; https://doi.org/10.3390/agriengineering5040147 - 11 Dec 2023
Abstract
Crown rot, caused by Fusarium species, is the most devastating postharvest disease in bananas. Fungicides are traditionally applied as a postharvest treatment to control crown rot in bananas. However, there is a need to research environmentally friendly compounds as postharvest treatments instead of
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Crown rot, caused by Fusarium species, is the most devastating postharvest disease in bananas. Fungicides are traditionally applied as a postharvest treatment to control crown rot in bananas. However, there is a need to research environmentally friendly compounds as postharvest treatments instead of chemical fungicides. The phenolic compounds gallic acid, protocatechuic acid, and chlorogenic acid were identified in coconut mesocarp extract. Overall, the treatments were more efficient in crown-based than fruit-based culture mediums. The mycelial development was inhibited in a range from 20 to 26% (applying coconut mesocarp extract at 5%) compared to the control. Sporulation and spore germination were significantly inhibited, with a reduction of 88% in spore production and 91% in spore germination inhibition compared to the control. In in vivo tests, the aqueous extracts were effective by limiting the percentage of infected fruit, crown rot, and fruit severity. The use of coconut mesocarp extracts can be an effective and environmentally friendly alternative to the use of fungicides for controlling Fusarium musae on bananas.
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(This article belongs to the Special Issue Novel Methods for Food Product Preservation)
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Open AccessArticle
A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops
AgriEngineering 2023, 5(4), 2381-2394; https://doi.org/10.3390/agriengineering5040146 - 11 Dec 2023
Abstract
During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The
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During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.
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(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)
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A Convolutional Neural Network Algorithm for Pest Detection Using GoogleNet
AgriEngineering 2023, 5(4), 2366-2380; https://doi.org/10.3390/agriengineering5040145 - 08 Dec 2023
Abstract
The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a
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The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run.
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(This article belongs to the Special Issue Application of Artificial Neural Network in Agriculture)
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Open AccessArticle
Thermal Performance of a Flat-Plate Solar Collector for Drying Agricultural Crops
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, , , , and
AgriEngineering 2023, 5(4), 2349-2365; https://doi.org/10.3390/agriengineering5040144 - 06 Dec 2023
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In this study, the finite volume method was used to evaluate the thermal performance of a flat-plate solar collector used to dry agricultural crops. A 3D numerical model was created and used to predict the outlet air velocities and temperatures for three inlet
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In this study, the finite volume method was used to evaluate the thermal performance of a flat-plate solar collector used to dry agricultural crops. A 3D numerical model was created and used to predict the outlet air velocities and temperatures for three inlet air velocities. When compared with experimental measurements, the numerical predictions showed good agreement under all testing conditions. Then, the numerical model was used to predict the internal airflow and heat transfer characteristics of the collector. The internal baffles were found to increase the dwell time and efficiency but also promote flow separation, which resulted in flow loss. In addition, the collector has a transparent cover that results in a substantial heat loss, which can be mitigated by adding a vacuum gap between the flow inside the collector and the cover. Increasing the flow rate increased the heat loss and decreased the heat uptake, which decreased the temperature difference between the inlet and outlet of the collector. Because the heat was lost through long-wavelength radiation via the transparent cover and sidewalls, coating the absorber plate with black matte paint to increase the solar radiation absorption coefficient did not improve the drying performance.
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Open AccessArticle
Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture
by
, , , , , , , , and
AgriEngineering 2023, 5(4), 2326-2348; https://doi.org/10.3390/agriengineering5040143 - 06 Dec 2023
Abstract
The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based
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The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.
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(This article belongs to the Special Issue Application of Geographic Information System and Remote Sensing Technology in Agricultural and Forestry Research)
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Thermal Conditions of Laying Quail Sheds in Brazil
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, , , , and
AgriEngineering 2023, 5(4), 2314-2325; https://doi.org/10.3390/agriengineering5040142 - 06 Dec 2023
Abstract
This study was conducted to survey the level of technification of quail sheds in Brazil. Data from 25 quail farms (5 in each Brazilian region) were collected by image analysis of videos available on the Internet. The analyzed variables were farm location, degree
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This study was conducted to survey the level of technification of quail sheds in Brazil. Data from 25 quail farms (5 in each Brazilian region) were collected by image analysis of videos available on the Internet. The analyzed variables were farm location, degree of technological adoption in quail sheds, housing conditions, structural conditions, wall conditions, and thermal comfort equipment. The data were subjected to descriptive analysis, and differences were assessed using the chi-squared test (p < 0.10). It was found that curtain walls were the most used system for air entry and renewal in quail sheds. Fan systems were present in only 12% of sheds, and evaporative cooling systems (or air conditioning) were observed in 4% of sheds, exclusively on large farms. Internal insulation was used in 20.83% of farms. In conclusion, Brazilian quail sheds have a low degree of technification; about 90% do not use implements such as ceiling, ventilation, and cooling systems. These conditions make it difficult to control environmental variables within quail sheds, impairing thermal comfort and, consequently, animal welfare and quail productivity.
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(This article belongs to the Section Livestock Farming Technology)
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Modeling of Coffee Fruit: An Approach to Simulate the Effects of Compression
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, , , and
AgriEngineering 2023, 5(4), 2303-2313; https://doi.org/10.3390/agriengineering5040141 - 01 Dec 2023
Abstract
The flavor, aroma, and color of coffee can be changed due to mechanical damage, reducing its quality. To measure the mechanical behavior of the fruit, compression tests can be performed at different stages of ripeness. In this study, we analyzed the deformation, strain
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The flavor, aroma, and color of coffee can be changed due to mechanical damage, reducing its quality. To measure the mechanical behavior of the fruit, compression tests can be performed at different stages of ripeness. In this study, we analyzed the deformation, strain energy, and von Mises stress of coffee fruits at mature, semi-mature, and immature stages under compression forces. Compression in three directions (x, y, and z) was simulated on coffee fruit models using the finite element method. A compression support was applied in the opposite direction to the force application axis. Numerical simulations of the compression process allowed us to verify that the more mature the fruit, greater the associated mean deformation (2.20 mm mm−1, 0.78 mm mm−1, and 0.88 mm mm−1), the lower the mean strain energy (0.07 mJ, 0.21 mJ, and 0.34 mJ), and the lower the mean equivalent von Mises stress (0.25 MPa, 1.03 MPa, and 1.25 MPa), corresponding to ripe, semi-ripe, and immature fruits, respectively. These analyses not only save time and professional resources but also offer insights into how strain energy and von Mises stress affect fruits at different maturation stages. This information can guide machine adjustments to reduce coffee harvesting damages.
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(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications)
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