Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42n3e20210126/2022
Iara J. S. Ferreira, S. Turco, Rodrigo T. Silva, S. T. D. Freitas, D. D. S. Costa
{"title":"EFFECTS OF COMMERCIAL RAPID COOLING PROGRAMS ON ‘ROSA’ MANGO QUALITY","authors":"Iara J. S. Ferreira, S. Turco, Rodrigo T. Silva, S. T. D. Freitas, D. D. S. Costa","doi":"10.1590/1809-4430-eng.agric.v42n3e20210126/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42n3e20210126/2022","url":null,"abstract":"","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67255888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42n5e20220027/2022
R. Saath, G. S. Wenneck, Roberto Rezende, D. C. Santi, L. L. D. Araújo
{"title":"BIOMETRY AND ESSENTIAL OIL OF OREGANO GROWN UNDER DIFFERENT WATER DEPTHS AND ORGANIC FERTILIZER DOSES IN A PROTECTED ENVIRONMENT","authors":"R. Saath, G. S. Wenneck, Roberto Rezende, D. C. Santi, L. L. D. Araújo","doi":"10.1590/1809-4430-eng.agric.v42n5e20220027/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42n5e20220027/2022","url":null,"abstract":"","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42nepe20220093/2022
Ibdal Satar, Adidiya Permadi, W. Ahmed
{"title":"EVALUATING PHYSICOCHEMICAL PROPERTIES OF MUTATED JATROPHA CURCAS SEED OILS AND THEIR FEASIBILITY AS NEW FEEDSTOCKS FOR BIODIESEL PRODUCTION","authors":"Ibdal Satar, Adidiya Permadi, W. Ahmed","doi":"10.1590/1809-4430-eng.agric.v42nepe20220093/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20220093/2022","url":null,"abstract":"","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42nepe20210101/2022
G. I. Gadotti, C. Ascoli, Ruan Bernardy, R. D. C. M. Monteiro, R. D. Pinheiro
The seed germination and vigor evaluation are essential for the sowing sector to measure the performance of different seed lots and improve the efficiency of storage and sowing processes. However, the analysis of various tests to determine seed quality generates a large amount of information, making it almost impossible for humans to perform a quick and effective quality control analysis. Therefore, the objective of this study was to evaluate the differences in the physiological quality of soybean seeds in different cultivars using machine learning techniques to rank the lots based on their quality. Three cultivars were used, and the analysis was germination, accelerated aging, tetrazolium treatment, seedling emergence, and 1000 seed weight from 65 lots were measured. The lots were evaluated in two phases, one immediately after harvest and the other after six months of storage. Random forest, multi-layer perceptron, J48, and classification via regression classifiers were used, aided by the feature resampler technique. Random forest and
{"title":"MACHINE LEARNING FOR SOYBEAN SEEDS LOTS CLASSIFICATION","authors":"G. I. Gadotti, C. Ascoli, Ruan Bernardy, R. D. C. M. Monteiro, R. D. Pinheiro","doi":"10.1590/1809-4430-eng.agric.v42nepe20210101/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20210101/2022","url":null,"abstract":"The seed germination and vigor evaluation are essential for the sowing sector to measure the performance of different seed lots and improve the efficiency of storage and sowing processes. However, the analysis of various tests to determine seed quality generates a large amount of information, making it almost impossible for humans to perform a quick and effective quality control analysis. Therefore, the objective of this study was to evaluate the differences in the physiological quality of soybean seeds in different cultivars using machine learning techniques to rank the lots based on their quality. Three cultivars were used, and the analysis was germination, accelerated aging, tetrazolium treatment, seedling emergence, and 1000 seed weight from 65 lots were measured. The lots were evaluated in two phases, one immediately after harvest and the other after six months of storage. Random forest, multi-layer perceptron, J48, and classification via regression classifiers were used, aided by the feature resampler technique. Random forest and","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42nepe20210123/2022
A. Christoforo, M. H. M. Moraes, I. F. Fraga, W. M. Pereira Junior, F. Lahr
The use of wood is widespread in rural constructions, and the truss systems stand out among its various applications. This specific system has several typologies and requires a thorough study to determine the most advantageous model for each project. The present study aims to apply the Computational Intelligence concepts to determine the minimum viable cross-section of a Howe truss. For the computational simulation, methods like Finite Elements were used to obtain the loads and the Firefly Algorithm for the optimization process, focusing on minimizing the total weight of the structural part. Studies were conducted varying the spans of the elements and the height to span ratio. The design assumptions for establishing the optimization method’s constraints follow the recommendations of the Brazilian standard for wood design, ABNT NBR 7190 (1997). Weights between 95.42 kg and 653.57
{"title":"COMPUTATIONAL INTELLIGENCE APPLIED IN OPTIMAL DESIGN OF WOODEN PLANE TRUSSES","authors":"A. Christoforo, M. H. M. Moraes, I. F. Fraga, W. M. Pereira Junior, F. Lahr","doi":"10.1590/1809-4430-eng.agric.v42nepe20210123/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20210123/2022","url":null,"abstract":"The use of wood is widespread in rural constructions, and the truss systems stand out among its various applications. This specific system has several typologies and requires a thorough study to determine the most advantageous model for each project. The present study aims to apply the Computational Intelligence concepts to determine the minimum viable cross-section of a Howe truss. For the computational simulation, methods like Finite Elements were used to obtain the loads and the Firefly Algorithm for the optimization process, focusing on minimizing the total weight of the structural part. Studies were conducted varying the spans of the elements and the height to span ratio. The design assumptions for establishing the optimization method’s constraints follow the recommendations of the Brazilian standard for wood design, ABNT NBR 7190 (1997). Weights between 95.42 kg and 653.57","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42nepe20210138/2022
Felipe S. M. Barroso, M. Inácio, Flávio G. Oliveira
This paper describes the modeling, implementation, and evaluation of a control system based on an embedded fuzzy controller for application in irrigation systems. The motivation for the development of this system comes from the need to offer farmers resources that provide a reduction in water and electricity consumption in irrigation, which contributes to reducing production costs and preserving natural resources. The proposed control system aims to keep the water flow constant, as close as possible to the desired value and independent of load variations. A single-board computer, with the fuzzy controller software, a frequency inverter to control the speed of the irrigation motor pump system, and a flow sensor to measure the water flow, was used to implement the system. The proposed control system was evaluated in laboratory and field experiments to simulate real operating conditions. The results showed that the system presents satisfactory performance, representing a viable alternative for application in general irrigation systems.
{"title":"EMBEDDED FUZZY CONTROLLER FOR APPLICATION IN IRRIGATION SYSTEMS","authors":"Felipe S. M. Barroso, M. Inácio, Flávio G. Oliveira","doi":"10.1590/1809-4430-eng.agric.v42nepe20210138/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20210138/2022","url":null,"abstract":"This paper describes the modeling, implementation, and evaluation of a control system based on an embedded fuzzy controller for application in irrigation systems. The motivation for the development of this system comes from the need to offer farmers resources that provide a reduction in water and electricity consumption in irrigation, which contributes to reducing production costs and preserving natural resources. The proposed control system aims to keep the water flow constant, as close as possible to the desired value and independent of load variations. A single-board computer, with the fuzzy controller software, a frequency inverter to control the speed of the irrigation motor pump system, and a flow sensor to measure the water flow, was used to implement the system. The proposed control system was evaluated in laboratory and field experiments to simulate real operating conditions. The results showed that the system presents satisfactory performance, representing a viable alternative for application in general irrigation systems.","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"5 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42n3e20200191/2022
J. T. D. Oliveira, R. A. D. Oliveira, E. Almeida, F. F. Cunha, P. Teodoro
{"title":"SPATIAL VARIABILITY OF ECOPHYSIOLOGICAL AND PRODUCTION COMPONENTS IN IRRIGATED NOBLE GARLIC","authors":"J. T. D. Oliveira, R. A. D. Oliveira, E. Almeida, F. F. Cunha, P. Teodoro","doi":"10.1590/1809-4430-eng.agric.v42n3e20200191/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42n3e20200191/2022","url":null,"abstract":"","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67255813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42n4e20220050/2022
D. Lourençoni, Déborah C. T. C. de Brito, P. D. Oliveira, S. Turco, Jeonan da S. Cunha
{"title":"FUZZY CONTROLLER APPLIED TO TEMPERATURE ADJUSTMENT IN INCUBATION OF FREE-RANGE EGGS","authors":"D. Lourençoni, Déborah C. T. C. de Brito, P. D. Oliveira, S. Turco, Jeonan da S. Cunha","doi":"10.1590/1809-4430-eng.agric.v42n4e20220050/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42n4e20220050/2022","url":null,"abstract":"","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42n5e20220043/2022
Luana C. Menegassi, Vinicius C. Benassi, L. R. Trevisan, F. Rossi, T. M. Gomes
Lowland rice is traditionally irrigated by flood systems, demanding high water consumption. Localized irrigation by subsurface dripping is proposed as an alternative, in addition to the replacement of the source of water intended for human consumption with another of lower quality, such as saline water. However, plants can be affected by saline and/or water stress in both conditions, and the use of thermal imaging emerges as a tool to assess the plant status. In this context, this study aimed to identify the stress of subsurface drip-irrigated arborio rice under different salt concentrations and soil moisture by thermographic images. The design consisted of randomized blocks in a (2×4)+2 factorial with three replications, totaling 30 experimental plots. Soil solution salinity was assessed by electrical conductivity. The thermal images were processed by an algorithm to determine the normalized relative canopy temperature (NRCT) index at different crop development stages. Saline stress was identified by the NRCT index, with higher sensitivity of plants at the flowering stage with a rebalance over time, confirmed at grain filling and harvest stages.
{"title":"THERMAL IMAGING FOR STRESS ASSESSMENT IN RICE CULTIVATION DRIP-IRRIGATED WITH SALINE WATER","authors":"Luana C. Menegassi, Vinicius C. Benassi, L. R. Trevisan, F. Rossi, T. M. Gomes","doi":"10.1590/1809-4430-eng.agric.v42n5e20220043/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42n5e20220043/2022","url":null,"abstract":"Lowland rice is traditionally irrigated by flood systems, demanding high water consumption. Localized irrigation by subsurface dripping is proposed as an alternative, in addition to the replacement of the source of water intended for human consumption with another of lower quality, such as saline water. However, plants can be affected by saline and/or water stress in both conditions, and the use of thermal imaging emerges as a tool to assess the plant status. In this context, this study aimed to identify the stress of subsurface drip-irrigated arborio rice under different salt concentrations and soil moisture by thermographic images. The design consisted of randomized blocks in a (2×4)+2 factorial with three replications, totaling 30 experimental plots. Soil solution salinity was assessed by electrical conductivity. The thermal images were processed by an algorithm to determine the normalized relative canopy temperature (NRCT) index at different crop development stages. Saline stress was identified by the NRCT index, with higher sensitivity of plants at the flowering stage with a rebalance over time, confirmed at grain filling and harvest stages.","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1590/1809-4430-eng.agric.v42nepe20210160/2022
Iara J. S. Ferreira, Sarah L. F. de O. Almeida, Acácio Figueiredo Neto, D. D. S. Costa
This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of 'Pacovan' bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R 2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R 2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of 'Pacovan' bananas.
{"title":"DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING","authors":"Iara J. S. Ferreira, Sarah L. F. de O. Almeida, Acácio Figueiredo Neto, D. D. S. Costa","doi":"10.1590/1809-4430-eng.agric.v42nepe20210160/2022","DOIUrl":"https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20210160/2022","url":null,"abstract":"This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of 'Pacovan' bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R 2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R 2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of 'Pacovan' bananas.","PeriodicalId":49078,"journal":{"name":"Engenharia Agricola","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67256529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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