Sensoriamento Remoto Aplicado a Modelagem De Produtividade Da Cultura Da Cana-De-Açúcar

Leda, Victor Costa; Golçalves, Aline Kuramoto; Lima, Natália da Silva

doi:10.17224/energagric.2019v34n2p263-270

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…[164] models, map vineyard variability, map grasslands biomass, and plan the wheat harvest among others [3]. Certain main limits based on orbital images were lacking the measurement accuracy and ground truth data of the agronomical variables.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

Shakir

2022

Journal of Smart Internet of Things

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Crop monitoring is a process that involves regular field visits that seem to be difficult since it needs a huge amount of time and manpower. Thus, in modern agriculture, with an extensive range of satellite data such as Landsat, Sentinel-2, Modis, and Palsar, data are readily available. Sugarcane is a tall perennial grass belonging to the genus Saccharum, utilized for producing sugar. These plants were generally 2–6 m tall with fibrous, stout, jointed stalks, rich in sucrose, that will be accumulated in the stalk internodes. Sugarcanes have a different growth pattern and phenology than many other crops; thus, the spectral and temporal features of satellite data are examined by utilizing statistical and machine learning (ML) techniques for optimal discrimination of sugarcane fields with other crops. In this study, we propose an Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring (ODLD-SSCM) model on Remote Sensing Images. The presented ODLD-SSCM model mainly intends to estimate the crop yield of sugarcanes using RSIs. In the presented ODLD-SSCM technique, the sugarcane yield mapping can be derived by the use of the self-attentive deep learning (SADL) model. Besides, an oppositional spider colony optimization (OSCO) algorithm is used for the hyperparameter tuning of the ODLD-SSCM model. A detailed set of experimentations were performed to demonstrate the enhanced outcomes of the ODLDSSCM model. A comprehensive comparison study pointed out the enhancements of the ODLD-SSCM model over other recent approaches.

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Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

Shakir

2022

Journal of Smart Internet of Things

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“…Traveling gun irrigation is a sprinkler system intensively used in sugarcane cultivation to apply water and vinasse (ANA, 2017). According to Silva et al (2015) and Leda et al (2019), this cultivation has high economic value for Brazilian agribusiness, allowing its use in the production of sugar, alcohol, and energy.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Wetted Sector Angle in Water Distribution of Traveling Gun Irrigation Systems

Prado

Colombo

2020

Eng. Agríc.

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This work aimed to simulate the water distribution of traveler gun irrigation, working with asymmetrical wetted angles and in windy conditions. In the simulations of a sprinkler with uniform radial profile, the following were considered: i) wetted angles between 180 and 330º; ii) wetted angle asymmetries from 0 to 90º; iii) wind speeds from 0 to 4.5 m s-1 ; and iv) wind direction of 0, 45, and 90º. Christiansen uniformity coefficients (CU) were calculated for strip widths from 30 to 100% of the sprinkler wetted diameter (WD). Regardless of the wetted angle adjustment, irrigations under wind speeds of 4.5 m s-1 should be avoided and strip spacings of less than 50% WD provide high CU for wind speeds up to 3.0 m s-1. For any wind conditions, asymmetries in the wetted angle led to a reduction in CU for a wetted angle of 210º. In the range of wetted angle from 270 to 330º, asymmetric adjustments of up to 20º provided a higher CU than 80% for a wind speed of 3 m s-1 and directions diagonal and perpendicular to the travel path associated with strip spacings between 60 to 70% WD, which are the spacings recommended by manufacturers of traveler irrigation machines.

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Sensor-Based Technologies in Sugarcane Agriculture

et al. 2022

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Sensoriamento Remoto Aplicado a Modelagem De Produtividade Da Cultura Da Cana-De-Açúcar

Cited by 4 publications

References 4 publications

Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

Asymmetric Wetted Sector Angle in Water Distribution of Traveling Gun Irrigation Systems

Sensor-Based Technologies in Sugarcane Agriculture

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