AgriEngineering最新文献

{"title":"Evaluation of the Accuracy of the Remote Determination of the Brewster Angle when Measuring Physicochemical Parameters of Soil","authors":"Gennadiy Ivanovich Linets, Anatoliy Vyacheslavovich Bazhenov, Sergey Vladimirоvich Malygin, Natalia Vladimirovna Grivennaya, Sergey Vladimirovich Melnikov, Vladislav Dmitrievich Goncharov","doi":"10.3390/agriengineering5040116","DOIUrl":"https://doi.org/10.3390/agriengineering5040116","url":null,"abstract":"In precision farming technology, the moisture of the soil, its granulometric composition, specific conductivity and a number of other physical and chemical parameters are determined using remote radar sensing. The most important parameters are those measured in the area of the plant root system located well below the “air-surface” boundary. In order to create conditions for the penetration of electromagnetic waves through the “air-surface” interface with a minimum reflection coefficient, the irradiation of the Earth’s surface is carried out obliquely with an angle of incidence close to the Brewster angle. The reflection coefficient, and, consequently, the Brewster angle, depend on the complex dielectric permittivity of the surface soil layer and are not known a priori. To determine the Brewster angle, the usual method is to search for the minimum amplitude of the vertically polarized signal reflected from the surface. Another approach is when the first derivative of the dependence of the modulus of the complex amplitude of a vertically polarized interference wave, taken with respect to the angle of incidence, is set equal to zero. In turn, in real dielectrics such as agricultural soils, the amplitude of the vertically polarized signal reflected from the surface is directly proportional to the reflection coefficient and does not have a pronounced minimum, which reduces the accuracy of the measurements. Based on the solution of the Helmholtz wave equation for a three-layered structure of the propagation medium (air, upper fertile soil layer, soil layer below the groundwater level), a model of the process of forming an interference wave under oblique irradiation of a planar layered dielectric with losses has been developed. Using the developed model, factors influencing the accuracy of determining the Brewster angle have been identified. For the first time, it is proposed to use the phase shift between the oscillations of the interference waves with vertical and horizontal polarization to measure the Brewster angle. A comparative assessment of the accuracy of determining the Brewster angle using known amplitude methods and the proposed phase method has been carried out. The adequacy of the method was experimentally confirmed. Recommendations have been developed for the practical application of the phase method of finding the Brewster angle for assessing the dielectric permittivity of soil and its moisture content.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the Efficacy of a Prototype That Combines Ultrasound and Pulsed Electric Field for Extracting Valuable Compounds from Mitragyna speciosa Leaves","authors":"Raweeroj Jintawiwat, Natnakorn Punamorntarakul, Rossakornpat Hirunyasiri, Parkpoom Jarupoom, Tanachai Pankasemsuk, Supakiat Supasin, Arthitaya Kawee-ai","doi":"10.3390/agriengineering5040115","DOIUrl":"https://doi.org/10.3390/agriengineering5040115","url":null,"abstract":"This work aimed to test the efficacy of an ultrasound (US) and pulsed electric field (PEF) apparatus to extract mitragynine from dried Mitragyna speciosa cv. Karn Dang leaves. Four modes of the device were tested: PEF, US, US + PEF, and PEF + US, and the modes were compared using a conventional technique (maceration, as the control). The liquid chromatography/mass spectrometry (LC-MS/MS) analysis revealed that the mitragynine contents from M. speciosa leaves using the four different modes were significantly different (p < 0.05). The highest extraction (106.63 ± 0.85 mg/L) of mitragynine was obtained by the mode using a combination of PEF + US, followed by US + PEF (97.27 ± 1.33 mg/L), with increased extraction efficiencies of 45.81 ± 0.59% and 33.00 ± 1.85%, respectively. Moreover, the total energy consumption under the combination technique was 25.0% lower than that with PEF assistance. Scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) were used to analyze the structural and functional features of the alterations in M. speciosa leaves. This study demonstrated that a combination of PEF and US devices may be regarded as a green alternative technique and can assist in streamlining the implementation of agricultural products.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135728779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of a Wet Medium Made of Mangosteen Peels for a Direct Evaporative Cooling System","authors":"Nattawut Chaomuang, Thanut Nuangjamnong, Samak Rakmae","doi":"10.3390/agriengineering5040114","DOIUrl":"https://doi.org/10.3390/agriengineering5040114","url":null,"abstract":"The present study aimed to investigate an alternative evaporative cooling pad material made from mangosteen peel (MP) waste. Mangosteen peels were used to fill a 150 mm thick mesh container with a packing density of 180 kg/m3. A wind tunnel was constructed and utilized to experimentally evaluate the cooling performance of this organic-waste-based pad under hot and humid conditions (31–34 °C and 55–70% RH). The performance parameters assessed included pressure drop, temperature drop, saturation effectiveness, cooling capacity, and coefficient of performance (COP). The influence of air velocity (0.7, 1.0, 1.4, and 1.8 m/s) on these parameters was also examined. The results revealed that the saturation effectiveness of the MP pad ranged from 53% to 77% within the considered air velocity range. The maximum temperature drop (4.6 °C), saturation effectiveness (77%), cooling capacity (0.6 kW), and COP (3.5) were achieved when the system operated at 1.4 m/s. A comparative study showed that, at this velocity, the MP pad provided performance nearly equivalent to that of the commercial cellulose paper pad, except for the pressure drop. This result affirms the potential of mangosteen peels as a suitable wet medium for evaporative cooling applications.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Neural Networks for Modeling and Optimizing Egg Cost in Second-Cycle Laying Hens Based on Dietary Intakes of Essential Amino Acids","authors":"Walter Morales-Suárez, Luis Daniel Daza, Henry A. Váquiro","doi":"10.3390/agriengineering5040112","DOIUrl":"https://doi.org/10.3390/agriengineering5040112","url":null,"abstract":"Egg production is a significant source of animal protein for human consumption. Feed costs significantly impact the profitability of egg production, representing more than 70% of the variable costs. This study evaluated the effect of dietary intakes of three essential amino acids (EAAs) on the egg cost for H&amp;N Brown second-cycle laying hens. The hens were fed for 20 weeks with 23 diets that varied in their lysine, methionine + cystine, and threonine contents. These amino acids were derived from both dietary and synthetic sources. Zootechnical results were used to calculate the feed cost per kilogram of egg (FCK), considering the cost of raw materials and the diet composition. Multivariate polynomial models and artificial neural networks (ANNs) were validated to predict FCK as a function of the EAAs and time. The EAA intakes that minimize FCK over time were optimized using the best model, a cascade-forward ANN with a softmax transfer function. The optimal scenario for FCK (0.873 USD/kg egg) at 20 weeks was achieved at 943.7 mg lysine/hen-day, 858.3 mg methionine + cystine/hen-day, and 876.8 mg threonine/hen-day. ANNs could be a valuable tool for predicting the egg cost of laying hens based on the nutritional requirements. This could help improve economic efficiency and reduce the feed costs in poultry companies.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"568 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation of Leaves and Fruits of Tomato Plants by Dominance","authors":"Juan Pablo Guerra Ibarra, Francisco Javier Cuevas de la Rosa, Oziel Arellano Arzola","doi":"10.3390/agriengineering5040113","DOIUrl":"https://doi.org/10.3390/agriengineering5040113","url":null,"abstract":"The production of food generated by agriculture has been essential for civilizations throughout time. Tillage of fields has been supported by great technological advances in several areas of knowledge, which have increased the amount of food produced at lower costs. The use of technology applied to modern agriculture has generated a research area called precision agriculture, which has providing crops with resources in an exact amount at a precise moment as one of its most relevant objectives The data analysis process in precision agriculture systems begins with the filtering of the information available, which can come from sources such as images, videos, and spreadsheets. When the information source is digital images, the process is known as segmentation, which consists of assigning a category or label to each pixel of the analyzed image. In recent years, different algorithms of segmentation have been developed that make use of different pixel characteristics, such as color, texture, neighborhood, and superpixels. In this paper, a method to segment images of leaves and fruits of tomato plants is presented, which is carried out in two stages. The first stage is based on the dominance of one of the color channels over the other two, using the RGB color model. In the case of the segmentation of the leaves, the green channel dominance is used, whereas the dominance of red channel is used for the fruits. In the second stage, the false positives generated during the previous stage are eliminated by using thresholds calculated for each pixel that meets the condition of the first stage. The results are measured by applying performance metrics: Accuracy, Precision, Recall, F1-Score, and Intersection over Union. The results for segmentation of the fruit and leaves of the tomato plants with the highest metrics is Accuracy with 98.34% for fruits and Recall with 95.08% for leaves.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical Methods for the Detection of Plant Pathogens and Diseases (Review)","authors":"Sergey V. Gudkov, Tatiana A. Matveeva, Ruslan M. Sarimov, Alexander V. Simakin, Evgenia V. Stepanova, Maksim N. Moskovskiy, Alexey S. Dorokhov, Andrey Yu. Izmailov","doi":"10.3390/agriengineering5040110","DOIUrl":"https://doi.org/10.3390/agriengineering5040110","url":null,"abstract":"Plant diseases of an infectious nature are the reason for major economic losses in agriculture throughout the world. The early, rapid and non-invasive detection of diseases and pathogens is critical for effective control. Optical diagnostic methods have a high speed of analysis and non-invasiveness. The review provides a general description of such methods and also discusses in more detail methods based on the scattering and absorption of light in the UV, Vis, IR and terahertz ranges, Raman scattering and LiDAR technologies. The application of optical methods to all parts of plants, to a large number of groups of pathogens, under various data collection conditions is considered. The review reveals the diversity and achievements of modern optical methods in detecting infectious plant diseases, their development trends and their future potential.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship between Leaf Area Index and Yield Components in Farmers’ Paddy Fields","authors":"Naoyuki Hashimoto, Yuki Saito, Shuhei Yamamoto, Taro Ishibashi, Ruito Ito, Masayasu Maki, Koki Homma","doi":"10.3390/agriengineering5040108","DOIUrl":"https://doi.org/10.3390/agriengineering5040108","url":null,"abstract":"Estimation of rice yield components is required to optimize cultivation management in fields. The leaf area index (LAI) can be a parameter for this estimation, but it has not been evaluated in farmers’ fields. In this study, we analyzed the relationship between the LAI and yield components using data collected over a five-year period in farmers’ fields for the cultivar Hitomebore. Leaf area dynamics (LAD) were parameterized by fitting a growth function to the time-series data of LAI measured using a canopy analyzer. The contribution of LAD to yield components was analyzed using multiple regression. The LAIs at five points during the growing season (effective integrated temperatures of 200, 400, 600, 800, and 1000 °Cd) were calculated using the growth function and the relationship between them and the yield components were analyzed using linear regression. The results of the multiple regression analysis showed that all function parameters significantly affected the yield components at the 5% probability level, with the greatest contribution from the LAI. The LAI at effective integrated temperatures of 400 to 600 °Cd significantly affected most of the yield components. However, the correlation coefficients between the LAI and yield components were not high (R = 0.18–0.61). The LAIs at almost all periods significantly affected the grain number per panicle and 1000-grain weight at the 5% probability level. These results suggest that the LAI could be used for monitoring trends in yield components, while further research on the development of accurate estimation methods is needed.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal","authors":"Ghada Sahbeni, Balázs Székely, Peter K. Musyimi, Gábor Timár, Ritvik Sahajpal","doi":"10.3390/agriengineering5040109","DOIUrl":"https://doi.org/10.3390/agriengineering5040109","url":null,"abstract":"Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System of Counting Green Oranges Directly from Trees Using Artificial Intelligence","authors":"Matheus Felipe Gremes, Igor Rossi Fermo, Rafael Krummenauer, Franklin César Flores, Cid Marcos Gonçalves Andrade, Oswaldo Curty da Motta Lima","doi":"10.3390/agriengineering5040111","DOIUrl":"https://doi.org/10.3390/agriengineering5040111","url":null,"abstract":"Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming increasingly viable in solving agricultural problems. Thus, this work aims to count green oranges directly from trees through video footage filmed in line along a row of orange trees on a plantation. For the video image processing flow, a solution was proposed integrating the YOLOv4 network with object-tracking algorithms. In order to compare the performance of the counting algorithm using the YOLOv4 network, an optimal object detector was simulated in which frame-by-frame corrected detections were used in which all oranges in all video frames were detected, and there were no erroneous detections. Being the scientific and technological innovation the possibility of distinguishing the green color of the fruits from the green color of the leaves. The use of YOLOv4 together with object detectors managed to reduce the number of double counting errors and obtained a count close to the actual number of oranges visible in the video. The results were promising, with an mAP50 of 80.16%, mAP50:95 of 53.83%, precision of 0.92, recall of 0.93, F1-score of 0.93, and average IoU of 82.08%. Additionally, the counting algorithm successfully identified and counted 204 oranges, closely approaching the actual count of 208. The study also resulted in a database with an amount of 644 images containing 43,109 orange annotations that can be used in future works.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Visible Spectral Index and Soybean Plant Variables to Study Hidden Nematicide Phytotoxicity","authors":"Ernane Miranda Lemes, Maria Amélia dos Santos, Lísias Coelho, Samuel Lacerda de Andrade, Aline dos Santos Oliveira, Igor Diniz Pessoa, João Paulo Arantes Rodrigues Cunha","doi":"10.3390/agriengineering5040107","DOIUrl":"https://doi.org/10.3390/agriengineering5040107","url":null,"abstract":"Significant crop losses are due to plant-parasitic nematodes. Nematicides are expensive and potentially toxic to men, the environment, and plants. This study evaluated the hidden phytotoxicity effects of nematicides in soybeans. Two soybean cultivars (8473RSF and M7198IPRO) were evaluated with five nematicide treatments (biological, cadusaphos, abamectin, fluensulfone, and an untreated control) for changes in chlorophylls, biometrics, and spectral (TGI visible spectral index captured with a smartphone camera) variables to determine and anticipate the identification of plant stresses. Evaluations occurred 33, 47, and 66 days after sowing (DAS). The a/b chlorophyll proportion was greatest for M7198IPRO and cadusaphos. The chlorophyll variables did not present significant interactions or differences at 47 DAS, indicating that possible nematicide effects were transient and should be evaluated earlier than 33 DAS. Leaf area, leaf mass, and shoot mass were smaller for 8473RSF and outstanding for abamectin and fluensulfone. The response of the spectral index did not present significant interaction among the factors; however, at 33 and 47 DAS, the index was low for 8473RSF and lowest for cadusaphos only at 33 DAS. The correlations between the spectral index and other variables were significant and moderate for soybean total leaf area. Although no apparent phytotoxicity symptoms caused by nematicides were observed, the visible vegetation index generated using a smartphone camera can still improve crop management solutions.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135829382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}