An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology

Maldaner, Leonardo Felipe; Canata, Tatiana Fernanda; Molin, José Paulo

doi:10.1007/s12355-021-01050-x

Cited by 9 publications

(10 citation statements)

References 31 publications

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“…In a MG, information transfer through various connectivity channels such as WiFi, WiMax, the Internet, Ethernet, and ZigBee is subject to a certain amount of delay [152]. In the worst-case scenario, this latency in transmission links can lead to poor and unreliable MG functioning.…”

Section: Latency In Auxiliary Control Transmissionsmentioning

confidence: 99%

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Ahmed,

Basit,

e Mustafa

et al. 2023

IET Generation Trans & Dist

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Microgrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability.

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Section: Latency In Auxiliary Control Transmissionsmentioning

confidence: 99%

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Ahmed,

Basit,

e Mustafa

et al. 2023

IET Generation Trans & Dist

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Section: Data Acquisition With Aos and Yield Measurementmentioning

confidence: 99%

“…Yield data collected from maneuvers and roads were manually removed. Subsequently, the discrepant data were filtered using the software MapFilter V.2.0 (Piracicaba, Brazil) [33], using global filtering with a threshold of 100% [34]. Three data acquisition scenarios were evaluated: (i) using only data obtained on the face exposed to direct sunlight (A1); (ii) using only the data obtained on the face with indirect sun exposure (A2); and (iii) using the average of the two faces for the same plant (A3).…”

Section: Data Acquisition With Aos and Yield Measurementmentioning

confidence: 99%

“…Using the optimal scenarios of the sensor height and data acquisition, the AOS data obtained from C1 to C7 were filtered using the MapFilter software [33] and then interpolated using ordinary kriging and a 3 m × 3 m grid. The AOS data were filtered, using global filtering with a threshold of 100%, local filtering with a threshold of 10% and a radius of 10 m. The spatio-temporal relationship of AOS data to coffee crop yield was evaluated by comparing the AOS maps for C1, C2, C3, C4 and C5 to the yield map for Y2, (c) categorization of points according to plant side; (d) polygons created over the yield data point to sample the sensor data, obtaining the data for scenarios A1 (mean using only yellow points), A2 (mean using only black points) and A3 (mean using yellow and black points).…”

Section: Spatio-temporal Relationship Of Aos Data To Coffee Crop Yieldmentioning

confidence: 99%

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Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

et al. 2022

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Monitoring the spatial variability of agricultural variables is a main step in implementing precision agriculture practices. Active optical sensors (AOS), with their instrumentation directly on agricultural machines, are suitable and make it possible to obtain high-frequency data. This study aimed to evaluate the potential of AOS to map the spatial and temporal variability of coffee crop yields, as well as to establish guidelines for the acquisition of AOS data for sensing the sides of a coffee plant, allowing the evaluation of large commercial fields. The study was conducted in a commercial coffee area of 10.24 ha, cultivated with the Catuaí 144 variety. Data collection was performed with six Crop Circle ACS 430 sensors (Holland Scientific, Lincoln, NE, USA) and two N-Sensor NG sensors (Yara International, Dülmen, Germany). Seven field expeditions were made to collect data using the optical sensors during 2019 and 2021, obtaining data during the flowering, fruit-filling and fruit maturation phases (pre-harvest), and post-harvest. The results showed that the different faces of the same plant present a different Pearson’s correlation coefficient (r) to its yield, obtained with a yield monitor on the harvester. The face with the highest exposure to solar radiation presented a slightly higher correlation to yield (−0.34 ≤ r ≤ −0.17) when compared with the face with less exposure (−0.27 ≤ r ≤ −0.15). In addition, it was observed that the vegetation indices measured at the beginning of the coffee cycle (before the rainy season that starts in October) present a positive correlation to the coffee yield of that same year (0.73 ≤ r ≤ 0.91). On the other hand, this relationship is changed after the beginning of the rain season, at which time the vegetation index increases abruptly, inverting the correlation with the yield after that (−0.93 ≤ r ≤ −0.77). Furthermore, it was observed that, due to the biennial nature of coffee production, the vegetation index acquired at a specific time has an inverted relationship when compared with the yield of that year and to the yield of the following (or previous) year.

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“…The figures evidence the fact that the coffee mass data obtained from the load cells allowed greater detailing of the small flow changes during harvest. This behavior can be explained due to the low frequency in the collection of data from the yield monitor, as already observed by Maldaner et al (2021b) when using an instrumented wagon to measure yield data in sugarcane harvesters.…”

Section: Quality Of Yield Datamentioning

confidence: 65%

Orbital, aerial, and proximal sensing applied to monitoring the spatial variability of coffee plantations

Martello¹

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Orbital, aerial, and proximal sensing applied to monitoring the spatial variability of coffee plantations / Maurício Martello. --Piracicaba, 2022. p.Tese (Doutorado) --USP / Escola Superior de Agricultura "Luiz de Queiroz".1. Agricultura de precisão 2. Sensoriamento Remoto 3. Geoprocessamento 4. Colheita mecanizada I. Título My advisor Prof. Dr. José Paulo Molin for for accepting me to be part of his research team. Thank you for encouraging and supporting my ideas throughout my doctorate. You are an inspiration for me as a scientist, professor and person.The Coordination for the Improvement of Higher Education Personnel (CAPES), for granting the scholarship.To Terrena Agro, Yara, Jacto, Planet Scope and Guima Coffee companies, for providing the equipment, imagens, and experimental fields over the three years of research. Especially to Mauro Mara, who on behalf of Terrena supported the project from the beginning. To Evaldo, Lucimar, Ricardo, Mariana, Salvador, Renato, and other people from the Guima Café farm who supported the field activities, always helping in the exchange of experiences and sparing no effort to help with whatever was needed. To Dr. Gustavo Portz for all the setup help and tips for using the sensor provided by Yara.To all friends from Precision Agriculture Laboratory (LAP), including the guys from the mechanization and precision agriculture group (gMAP), for the pleasant working environment of the last years. In particular, Helizani C.

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An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology

Cited by 9 publications

References 31 publications

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

Orbital, aerial, and proximal sensing applied to monitoring the spatial variability of coffee plantations

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