AgriEngineering最新文献_第6页

A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees 由空间殖民算法生成的用于树木自动修剪策略的多标准决策方法

AgriEngineering Pub Date : 2024-02-26 DOI: 10.3390/agriengineering6010033

Gang Zhao, Dian Wang

{"title":"A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees","authors":"Gang Zhao, Dian Wang","doi":"10.3390/agriengineering6010033","DOIUrl":"https://doi.org/10.3390/agriengineering6010033","url":null,"abstract":"The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct three-dimensional tree characteristics and execute appropriate pruning strategies. Three-dimensional modeling plays a crucial role in enabling accurate pruning outcomes. This paper introduces a specialized tree modeling approach using the space colonization algorithm (SCA) tailored for pruning. The proposed method extends SCA to operate in three-dimensional space, generating comprehensive cherry tree models. The resulting models are exported as normalized point cloud data, serving as the input dataset. Multiple criteria decision analysis is utilized to guide pruning decisions, incorporating various factors such as tree species, tree life cycle stages, and pruning strategies during real-world implementation. The pruning task is transformed into a point cloud neural network segmentation task, identifying the trunks and branches to be pruned. This approach reduces the data acquisition time and labor costs during development. Meanwhile, pruning training in a virtual environment is an application of digital twin technology, which makes it possible to combine the meta-universe with the automated pruning of fruit trees. Experimental results demonstrate superior performance compared to other pruning systems. The overall accuracy is 85%, with mean accuracy and mean Intersection over Union (IoU) values of 0.83 and 0.75. Trunks and branches are successfully segmented with class accuracies of 0.89 and 0.81, respectively, and Intersection over Union (IoU) metrics of 0.79 and 0.72. Compared to using the open-source synthetic tree dataset, this dataset yields 80% of the overall accuracy under the same conditions, which is an improvement of 6%.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430255","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}

引用次数: 0

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area 基于无人机的间作仙人掌分类：在灌溉区使用机器学习对 RGB 和多光谱样本空间进行比较

AgriEngineering Pub Date : 2024-02-23 DOI: 10.3390/agriengineering6010031

Oto Barbosa de Andrade, A. Montenegro, Moisés Alves da Silva Neto, L. D. B. D. Sousa, T. Almeida, João L. M. P. de de Lima, Ailton Alves de Carvalho, Marcos Vinícius da Silva, Victor Wanderley Costa de Medeiros, Rodrigo Gabriel Ferreira Soares, T. G. F. Silva, B. P. Vilar

{"title":"UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area","authors":"Oto Barbosa de Andrade, A. Montenegro, Moisés Alves da Silva Neto, L. D. B. D. Sousa, T. Almeida, João L. M. P. de de Lima, Ailton Alves de Carvalho, Marcos Vinícius da Silva, Victor Wanderley Costa de Medeiros, Rodrigo Gabriel Ferreira Soares, T. G. F. Silva, B. P. Vilar","doi":"10.3390/agriengineering6010031","DOIUrl":"https://doi.org/10.3390/agriengineering6010031","url":null,"abstract":"Precision agriculture requires accurate methods for classifying crops and soil cover in agricultural production areas. The study aims to evaluate three machine learning-based classifiers to identify intercropped forage cactus cultivation in irrigated areas using Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between multispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficiency analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The classification targets included exposed soil, mulching soil cover, developed and undeveloped forage cactus, moringa, and gliricidia in the Brazilian semiarid. The results indicated that the KNN and RF algorithms outperformed other methods, showing no significant differences according to the kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed well, with individual accuracy rates above 85% for both sample spaces. Overall, the KNN algorithm demonstrated superiority for the RGB sample space, whereas the RF algorithm excelled for the multispectral sample space. Even with the better performance of multispectral images, machine learning algorithms applied to RGB samples produced promising results for crop classification.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"36 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957298","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}

引用次数: 0

Glyphosate Pattern Recognition Using Microwave-Interdigitated Sensors and Principal Component Analysis 利用微波插入式传感器和主成分分析进行草甘膦模式识别

AgriEngineering Pub Date : 2024-02-23 DOI: 10.3390/agriengineering6010032

C. Santillán-Rodríguez, R. Sáenz-Hernández, Cristina Grijalva-Castillo, Eutiquio Barrientos-Juárez, J. T. Elizalde-Galindo, J. Matutes-Aquino

引用次数: 0

Assessment of a Low-Cost Hydrogen Sensor for Detection and Monitoring of Biohydrogen Production during Sugarcane Straw/Vinasse Co-Digestion 评估用于检测和监控甘蔗秸秆/蔗渣协同消化过程中生物制氢的低成本氢传感器

AgriEngineering Pub Date : 2024-02-22 DOI: 10.3390/agriengineering6010029

Andrés Barrera, David Gómez-Ríos, Howard Ramírez-Malule

{"title":"Assessment of a Low-Cost Hydrogen Sensor for Detection and Monitoring of Biohydrogen Production during Sugarcane Straw/Vinasse Co-Digestion","authors":"Andrés Barrera, David Gómez-Ríos, Howard Ramírez-Malule","doi":"10.3390/agriengineering6010029","DOIUrl":"https://doi.org/10.3390/agriengineering6010029","url":null,"abstract":"In this work, hydrogen production from the co-digestion of sugarcane straw and sugarcane vinasse in the dark fermentation (DF) process was monitored using a cost-effective hydrogen detection system. This system included a sensor of the MQ-8 series, an Arduino Leonardo board, and a computer. For the DF, different concentrations of sugarcane vinasse and volumetric ratios of vinasse/hemicellulose hydrolysate were used together with a thermally pretreated inoculum, while the hydrogen detection system stored the hydrogen concentration data during the fermentation time. The results showed that a higher concentration of vinasse led to higher inhibitors for the DF, resulting in a longer lag phase. Additionally, the hydrogen detection system proved to be a useful tool in monitoring the DF, showcasing a rapid response time, and providing reliable information about the period of adaptation of the inoculum to the substrate. The measurement system was assessed using the error metrics SE, RMSE, and MBE, whose values ranged 0.6 and 5.0% as minimum and maximum values. The CV (1.0–8.0%) and SD (0.79–5.62 ppm) confirmed the sensor’s robustness, while the ANOVA at the 5% significance level affirmed the repeatability of measurements with this instrument. The RMSE values supported the accuracy of the sensor for online measurements (6.08–14.78 ppm). The adoption of this straightforward and affordable method sped up the analysis of hydrogen in secluded regions without incurring the expenses associated with traditional measuring instruments while offering a promising solution for biomass valorization, contributing to the advancement of rural green energy initiatives in remote areas.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"19 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441608","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}

引用次数: 0

Maize Crop Detection through Geo-Object-Oriented Analysis Using Orbital Multi-Sensors on the Google Earth Engine Platform 通过在谷歌地球引擎平台上使用轨道多传感器进行面向地理对象的分析来探测玉米作物

AgriEngineering Pub Date : 2024-02-22 DOI: 10.3390/agriengineering6010030

Ismael Cavalcante Maciel Junior, R. Dallacort, Cácio Luiz Boechat, P. Teodoro, L. P. Teodoro, F. Rossi, J. F. Oliveira‐Júnior, João Lucas Della-Silva, F. Baio, Mendelson Lima, C. A. S. Silva Junior

{"title":"Maize Crop Detection through Geo-Object-Oriented Analysis Using Orbital Multi-Sensors on the Google Earth Engine Platform","authors":"Ismael Cavalcante Maciel Junior, R. Dallacort, Cácio Luiz Boechat, P. Teodoro, L. P. Teodoro, F. Rossi, J. F. Oliveira‐Júnior, João Lucas Della-Silva, F. Baio, Mendelson Lima, C. A. S. Silva Junior","doi":"10.3390/agriengineering6010030","DOIUrl":"https://doi.org/10.3390/agriengineering6010030","url":null,"abstract":"Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"8 s4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140439994","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}

引用次数: 0

Peculiarities of Unmanned Aerial Vehicle Use in Crop Production in Russia: A Review 俄罗斯农作物生产中使用无人驾驶飞行器的特殊性：综述

AgriEngineering Pub Date : 2024-02-21 DOI: 10.3390/agriengineering6010028

M. Zvezdina, Yu A Shokova, S. Lazarenko

引用次数: 0

Modelling the Yield and Estimating the Energy Properties of Miscanthus x Giganteus in Different Harvest Periods 建立不同收获期 Miscanthus x Giganteus 的产量模型并估算其能量特性

AgriEngineering Pub Date : 2024-02-19 DOI: 10.3390/agriengineering6010026

Ivan Brandić, N. Voća, J. Leto, N. Bilandzija

引用次数: 0

Theoretical Study of the Motion of a Cut Sugar Beet Tops Particle along the Inner Surface of the Conveying and Unloading System of a Topping Machine 切下的甜菜头颗粒沿打顶机输送和卸载系统内表面运动的理论研究

AgriEngineering Pub Date : 2024-02-15 DOI: 10.3390/agriengineering6010025

S. Pascuzzi, V. Bulgakov, I. Holovach, S. Ivanovs, A. Aboltins, Y. Ihnatiev, A. Rucins, O. Trokhaniak, Francesco Paciolla

{"title":"Theoretical Study of the Motion of a Cut Sugar Beet Tops Particle along the Inner Surface of the Conveying and Unloading System of a Topping Machine","authors":"S. Pascuzzi, V. Bulgakov, I. Holovach, S. Ivanovs, A. Aboltins, Y. Ihnatiev, A. Rucins, O. Trokhaniak, Francesco Paciolla","doi":"10.3390/agriengineering6010025","DOIUrl":"https://doi.org/10.3390/agriengineering6010025","url":null,"abstract":"One of the most delicate operations in the sugar beet harvesting process is removing the tops from the heads of the root crops without any mechanical damages. The aim of this study is to improve the design of the conveying and unloading system of the sugar beet topper machine. In this paper, a mathematical model of the motion of a cut beet tops particle M, along the conveying and unloading system, has been developed to support the evaluation of kinematic and design parameters, depending on the rotational speed of the thrower blade, the air flow speed, the required ejection speed of particle M, and the position of the trailer that moves alongside the harvester. It has been established that increasing the speed Va of the top particle M, which has left the end of the blade of the thrower, leads to an increase in the arc coordinate S(t) of its movement along the cylindrical section of the casing. Within the range of the speed change from 4 m·s–1 to 8 m·s–1, the value of the arc coordinate S(t) increases by 1.4 times during time t = 0.006 s. Moreover, a rapid decrease in speed V is observed with an increase in the length x of the discharge chute.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836192","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}

引用次数: 0

Theoretical Study of the Motion of a Cut Sugar Beet Tops Particle along the Inner Surface of the Conveying and Unloading System of a Topping Machine 切下的甜菜头颗粒沿打顶机输送和卸载系统内表面运动的理论研究

AgriEngineering Pub Date : 2024-02-15 DOI: 10.3390/agriengineering6010025

S. Pascuzzi, V. Bulgakov, I. Holovach, S. Ivanovs, A. Aboltins, Y. Ihnatiev, A. Rucins, O. Trokhaniak, Francesco Paciolla

{"title":"Theoretical Study of the Motion of a Cut Sugar Beet Tops Particle along the Inner Surface of the Conveying and Unloading System of a Topping Machine","authors":"S. Pascuzzi, V. Bulgakov, I. Holovach, S. Ivanovs, A. Aboltins, Y. Ihnatiev, A. Rucins, O. Trokhaniak, Francesco Paciolla","doi":"10.3390/agriengineering6010025","DOIUrl":"https://doi.org/10.3390/agriengineering6010025","url":null,"abstract":"One of the most delicate operations in the sugar beet harvesting process is removing the tops from the heads of the root crops without any mechanical damages. The aim of this study is to improve the design of the conveying and unloading system of the sugar beet topper machine. In this paper, a mathematical model of the motion of a cut beet tops particle M, along the conveying and unloading system, has been developed to support the evaluation of kinematic and design parameters, depending on the rotational speed of the thrower blade, the air flow speed, the required ejection speed of particle M, and the position of the trailer that moves alongside the harvester. It has been established that increasing the speed Va of the top particle M, which has left the end of the blade of the thrower, leads to an increase in the arc coordinate S(t) of its movement along the cylindrical section of the casing. Within the range of the speed change from 4 m·s–1 to 8 m·s–1, the value of the arc coordinate S(t) increases by 1.4 times during time t = 0.006 s. Moreover, a rapid decrease in speed V is observed with an increase in the length x of the discharge chute.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"115 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776769","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}

引用次数: 0

Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis 用于快速准确诊断番茄植物病害的增强型深度学习架构

AgriEngineering Pub Date : 2024-02-12 DOI: 10.3390/agriengineering6010023

Shahab Ul Islam, Shahab Zaib, G. Ferraioli, V. Pascazio, Gilda Schirinzi, Ghassan Husnain

{"title":"Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis","authors":"Shahab Ul Islam, Shahab Zaib, G. Ferraioli, V. Pascazio, Gilda Schirinzi, Ghassan Husnain","doi":"10.3390/agriengineering6010023","DOIUrl":"https://doi.org/10.3390/agriengineering6010023","url":null,"abstract":"Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"92 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842437","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}

引用次数: 0