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Revista Ciencias Técnicas Agropecuarias

versão On-line ISSN 2071-0054

Rev Cie Téc Agr vol.31 no.1 San José de las Lajas jan.-abr. 2022  Epub 12-Nov-2021

 

ORIGINAL ARTICLE

Intervention in Case IH A8000 Sugarcane Harvesters of Sugar Mill in Villa Clara, Cuba

Carlos A. Pérez-GarcíaI  * 
http://orcid.org/0000-0003-4781-6771

Alexander Rodríguez-ConteI 
http://orcid.org/0000-0003-3767-1708

Luis Hernández-SantanaI 
http://orcid.org/0000-0003-0558-3690

Miguel A. Rodríguez-OrozcoII 
http://orcid.org/0000-0002-3622-4693

Rafael Cruz-IglesiasIII 
http://orcid.org/0000-0002-7564-7779

José Luis Capote-FernándezIII 
http://orcid.org/0000-0002-3733-0893

IUniversidad Central “Marta Abreu” de Las Villas, Facultad de Ingeniería Eléctrica, Departamento de Control Automático, Santa Clara, Villa Clara, Cuba.

IIUniversidad Central “Marta Abreu” de Las Villas, Facultad de Ciencias Agropecuarias, Departamento de Ingeniería Agrícola, Santa Clara, Villa Clara, Cuba.

IIIUnidad Científico Técnica GEOCUBA Investigación y Consultoría, Cuba.

ABSTRACT

Increasing the productive yields of arable areas is one of the main tasks of researchers in the field. In this sense, agricultural machinery has been the object of application of existing technological development in areas such as positioning systems, integrated circuits, onboard computers, among others. For this reason, this type of machinery has now become one of the most important sources of information for the management of agricultural practices; the visualization, analysis, and storage of the high volume of information generated require the use of computational tools. Geographic Information Systems (GIS) are the most widespread platforms for the management of data exported by Advanced Farming System (AFS) agricultural machinery. The present work shows some of the potentialities of the use of AFS systems in Case IH A8000 sugarcane harvesters of “Héctor Rodríguez” Sugar Mill in Villa Clara Province. To this end, the SMS Advanced software is used to create maps and reports of the operations carried out by the sugar cane harvesters during their agricultural activities and a brief analysis of them is made. Finally, the feasibility of publishing the harvest data in the company's Spatial Data Infrastructure (AZCUBA’s SDI) is shown.

Keywords: Advanced Farming System; Yield Data; Integrated Circuit

INTRODUCTION

The rational and sustainable use of cultivable areas constitutes one of the premises for developing countries. In this sense, agriculture has been the object of application of advanced technologies in the area of ​​Positioning Systems, Integrated Circuits, on-board computers, among others. The use of these technologies allows obtaining a greater level of detail of the cultivable areas, which makes possible the establishment of new forms of management of plots based on the real needs of the crop.

Agricultural machinery is also continuously developed and is now one of the most important sources of information for data collection in the field (Heege, 2013). Cuba has allocated considerable resources to modernizing the technological equipment of strategic sectors of the economy; such is the case of the import of advanced agricultural machinery for the mechanized harvest of sugar cane (Gradaille-Daquinta et al., 2014). As part of the National Program for Automatic Science, Technology and Innovation, Robotics and Artificial Intelligence (ARIA + CAÑA) and its project "Automatic, robotics and artificial intelligence tools to increase the efficiency of sugarcane production" with the participation of the AZCUBA Sugar Company of Villa Clara, the Central University "Marta Abreu" of Las Villas (UCLV), the GEOCUBA Research and Consulting Technical Scientific Unit, and the Territorial Research Station of the Sugar Cane (ETICA) of Villa Clara, carried out an intervention on several Case IH A8800 sugarcane harvesters belonging to the “Héctor Rodríguez” Sugar Mill in Sagua la Grande Municipality.

The high efficiency rates of the Case IH are supported by an automated system made up of various sensors, actuators, control modules and, as the main interface with the operator, an on-board computer, also known as a performance monitor (Tumenjargal et al., 2017). This integrates the functionalities of supervising certain subsystems of the combine, as well as configuring the benefits of the Advanced Crop System (AFS), among which are: speed control, location based on the System Global Navigation Satellite (GNSS) and harvest data log (Perez-Garcia et al., 2018). In the case of the A8000 series combines, the present performance monitor is the AFS Pro 700; which is the result of the evolution of other monitors previously used by the manufacturers of this firm (López-Sandin & Herrero-Bello, 2018). From the correct configuration of it and its associated devices, it is possible to record important operating parameters of the machinery that allow, among others, to evaluate operations in the field.

In this report, the main results of the intervention in the harvesters under study are shown within the framework of the project that allows validating the efficiency of the collection system and the processing of the data obtained by the performance monitor, through the SMS Advanced software (Ag Leader Technology, 2021). In addition, the training tasks carried out for the management of harvest data to the personnel of “Héctor Rodríguez” Sugar Mill and its publication in the Spatial Data Infrastructure (IDE) developed for AZCUBA from a research and development project are described. As part of the training carried out to the company's personnel, some analyses of interest are carried out. They were trained in the use of SMS and potential solutions are proposed based on remote sensing to detected problems. Finally, a series of final considerations and recommendations are given for the system execution effectiveness.

MATERIALS AND METHODS

Study Areas

For the study, brigade number 6 of said entity that worked at UBPC Monte Lucas was selected. This brigade has a new technology equipment park consisting of three Case IH Austoft 8800 (A8800) sugarcane harvesters and six YTO 1604 moving tractors. The work area took place specifically in fields 9-10 and 1-10 of the blocks 1325 and 1326, respectively, which is located between the coordinates 22.818942 - 22.828478 of north latitude and 80.017433 - 79.991977 of west longitude (Figure 1)

FIGURE 1 Location of the study area. 

Work Methodology

The in-depth exploitation of the advanced agriculture functionalities present in the Case IH A8000 combines, starts from the correct configuration of the yield monitor and the positioning receiver present on the machine. This allows the periodic recording of variables of interest for the operation of the machine and the estimation of exploitation rates. Thus, measurements were collected like pressure of the base cutter, revolutions per minute of the primary and secondary extractors, the speed of advance, the working time, among others. To carry out this task, a field work was carried out, where the central staff was explained how to configure the AFS 700 monitors following the methodology proposed by Pérez (2016).

With the objective of strategic planning that contributes to sustainable development, AZUCUBA Business Group carried out an administrative structure of its cultivation areas divided according to physical fitness in Lot, Block and Field (Becerras et al., 2008). This information, managed by the land-use planning specialist of the sugar mill, has a spatial component that represents, from georeferenced polygons, the location of each plot (Benítez-Puig et al., 2018). In this context, company personnel were trained in the configuration required to import the work area zoning into the combine's performance monitor. The added value of this service consists of linking, in an automated way, the exported variables of the machinery with the administrative structure of the crop areas. Once this information is imported, the combine will detect the lot, block and field in which it is located, according to the information of its positioning.

A combine harvester in one shift can generate between 7,000 and 8,000 georeferenced data, which is roughly equivalent to 200,000 data per month. If this value is multiplied by the number of harvesters managed by AZCUBA and by the harvest months, the handling of this large volume of data constitutes a BigData problem.

To manage the harvest data exported by SMS from the IDE, a platform based on geospatial BigData technologies was used for the storage, processing and publication of vector data using Apache Hadoop, Apache Spark and Accumulo-GeoMesa (Capote-Fernández & Cruz-Iglesias, 2020). A web application was developed (https://movilweb.geocuba.cu/azcuba) that is part of the workflow during the processing of machinery information for publication in the IDE, to upload the exported files to a service that processes and inserts them into the platform. The data inserted in the BigData cluster is published through a map service based on Geoserver-GeoMesa, which by using a Web Map Services (WMS) interface (De la Beaujardiere, 2003) integrates harmoniously into the Generic IDE viewer. As part of the process, a metadata is created for each data package, with general information and the temporal and spatial extent. This metadata is published in a metadata catalog service of AZCUBA IDE, from where its search, retrieve and view are guaranteed.

The collection of the exported data was carried out by agreement, in 24-hour intervals, coinciding with the reading of the trajectory of the machinery from the positioning receiver installed additionally in the combines and tractors of the sugar company. It was found that this flow is viable, although the optimal way to carry out this process is each time the combined are supplied with fuel. The collection of harvest data in this time interval is practical because, in addition to coinciding with another similar process, the information on the cane cut by the operators can be obtained with the same sampling time. This makes possible to indirectly analyze the efficiency of tons cut per hectare and the fuel consumption per ton of cane cut for each combine. Associated with the availability of the two sources of information (harvest data and data from the external receiver), an experiment was carried out to assess the viability of inferring fuel consumption per ton of cane cut.

The harvest data exported by the machinery is nothing more than the measurement of the main operating parameters of the combines, georeferenced by the integrated positioning receiver of the machine. For the management, visualization and storage of this information, it is an essential requirement to use professional computer programs to decode the information contained therein. In this sense, the SMS Avanced software was used and sugar mill staff was trained in the complete installation and use of the computer programs

RESULTS AND DISCUSSION

Report Generation

For the management of the equipment of new harvesting technology, AZCUBA Business Group uses a “fleet control” system. That system is based on the measurement of the distance traveled, recorded by the receiver of positioning external to the combine harvester system and the fuel used derived from the relationship between: the volume of the tank of each machine and the dispatch made by the tank car. Finally, this information is uploaded to the MovilWeb platform in order to create the reports required for the management of the machinery (González-Suárez et al., 2018). In order to facilitate the procedure for adopting the new technology analyzed in this work, operation reports were prepared with the variables analyzed in the "fleet control" and others of interest to the Central managers were added (Figures 2 and 3).

FIGURE 2 Operation Report of Operator Roselio. 

FIGURE 3 Operation Report of Operator Ariel Hernandez. 

One of the variables of greatest interest to analyze the operational efficiency of agricultural machinery is fuel consumption (Ramos et al., 2016). In this case, the reports shown previously were generated in an automated way, with the aim of verifying that, despite the consumption of both machines being similar, the value differs with the external measurements of the "fleet control" in the order of 30 liters of fuel. In order to validate the sensing carried out by the harvester's own system, maps of the variable “engine load” were also made, since this parameter is closely related to fuel consumption. In Figure 4, the variable load of the motor of both harvesters is represented separately.

FIGURE 4 Map of the motor load variable A) Operator Roselio B) Operator Ariel Hernandez. 

Another parameter of special interest for the analysis of the efficiency of the machinery is the working time. To that end, work was carried out on the basis of the information recorded in an automated way by the system present in the combine harvester and a specific report was configured (Figures 5 and 6) to analyze field operations of the drivers. The Work Status variable represents in a Boolean way the status of the harvest modules (on-in or off-out).

FIGURE 5 Ariel Hernandez’s Operation Report. 

FIGURE 6 Roselio’s Operation Report. 

From the summaries shown previously, it is possible to verify that the machinery travels approximately 60% of the total distance with the harvesting devices deactivated. This may be due to trips made for maintenance work or to be located in work areas. Although these actions threaten the efficiency of the machines, they are not always due to bad operations by the driver, since it is necessary to plan cutting tasks based on optimal operating routes. In this sense, the work with aerial images, whether of Unmanned Aerial Vehicles (UAVs) or satellites, allows the application of remote sensing techniques for the establishment of optimal routes of operation from geometric measurements and even on the basis of the optimal state of the crop for harvest (Shelestov et al., 2013).

Integration with AZCUBA Spatial Data Infrastructure

The use of web platforms for the management of spatial data is of great importance for administrative decision-making by users who do not always have high knowledge of geomatics (Hernández, 2021). To this end, the sugar company personnel was trained to process the data exported by the harvester and make them compatible with the necessary standards to incorporate them into AZCUBA IDE, which can be consulted through the link https://azcuba.geocuba.cu/viewer (Figure 7).

FIGURE 7 AZCUBA IDE viewer. 

As a result of this action, it was possible to view remotely the harvest data exported by the Case IH A8800 under study. The integration of the harvest data with the zoning of the cultivation areas, together with the tools present in the IDE, made possible to know in real time, the current state of the arable fields and thus quantify the harvested areas and the rest. In the same way, a training of the company's specialist was carried out, for the use of the benefits of the web platform.

CONCLUSIONS

  • The Case IH A8000, equipped with the AFS Pro 700 performance monitors, allow the application of advanced cultivation techniques in the sugarcane harvest, providing valuable georeferenced information of the operations in the field.

  • The technical staff of “Héctor Rodríguez” Sugar Mill, trained in the use of the harvest data management software Spatial Management Systems (SMS), developed the AFS machinery farm management capacity, visualizing the work carried out at the field level, the productivity rates achieved and the fuel consumed in each operation, among others.

  • Managing the information exported by the Case IH A8000 combine performance monitor, allowed establishing the control strategies through automation in the generation of reports and variable maps.

  • The results obtained confirm that the use of the potentialities of the Advanced Cultivation System represents a viable tool for the management of agricultural machinery.

  • The publication of harvest data in AZCUBA IDE facilitates access to the information for multiple users, which lays the foundation for future data engineering work

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Received: February 02, 2021; Accepted: November 12, 2021

*Author for correspondence: Carlos A. Pérez-García, e-mail: capgarcia@uclv.cu, cpgarcia518@gmail.com

Carlos A. Pérez-García, Profesor, Universidad Central “Marta Abreu” de Las Villas, Facultad de Ingeniería Eléctrica, Departamento de Control Automático, Santa Clara, Villa Clara, Cuba, e-mail: capgarcia@uclv.cu, cpgarcia518@gmail.com

Alexander Rodríguez-Conte, Profesor, Universidad Central “Marta Abreu” de Las Villas, Facultad de Ingeniería Eléctrica, Departamento de Control Automático, Santa Clara, Villa Clara, Cuba, e-mail: arconte@uclv.cu

Luis Hernández-Santana, Profesor, Universidad Central “Marta Abreu” de Las Villas, Facultad de Ingeniería Eléctrica, Departamento de Control Automático, Santa Clara, Villa Clara, Cuba, e-mail: luishs@uclv.edu.cu

Miguel A. Rodríguez-Orozco, Profesor Titular, Universidad Central “Marta Abreu” de Las Villas, Facultad de Ciencias Agropecuarias, Departamento de Ingeniería Agrícola, Santa Clara, Villa Clara, Cuba, e-mail: miguelro@uclv.edu.cu

Rafael Cruz-Iglesias, Investigador, Unidad Científico Técnica GEOCUBA Investigación y Consultoría, Cuba, e-mail: rcruz@geomix.geocuba.cu

José Luis Capote-Fernández, Investigador, Unidad Científico Técnica GEOCUBA Investigación y Consultoría, Cuba, e-mail: capote@geomix.geocuba.cu

The authors of this work declare no conflict of interests.

AUTHOR CONTRIBUTIONS: Conceptualization: Carlos Alejandro Perez Garcia, Alexander Rodríguez Conte, Luis Hernández Santana, Miguel A. Rodríguez Orozco. Data curation: Carlos Alejandro Perez Garcia, Alexander Rodríguez Conte. Formal Analysis: Carlos Alejandro Perez Garcia, Miguel A. Rodríguez Orozco. Investigation: Carlos Alejandro Perez Garcia, Alexander Rodríguez Conte, Luis Hernández Santana, Miguel A. Rodríguez Orozco, Rafael Cruz Iglesias, José Luis Capote Fernández. Methodology: Luis Hernández Santana, Miguel A. Rodríguez Orozco, Rafael Cruz Iglesias, José Luis Capote Fernández. Supervision: Luis Hernández Santana, Miguel A. Rodríguez Orozco. Writing - original draft: Carlos Alejandro Perez Garcia. Writing - review & editing: Carlos Alejandro Perez Garcia, Miguel A. Rodríguez Orozco.

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