Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Canata, Tatiana Fernanda; Wei, Marcelo Chan Fu; Maldaner, Leonardo Felipe; Molin, José Paulo

doi:10.3390/rs13020232

Cited by 48 publications

(45 citation statements)

References 50 publications

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“…They analyze crop attributes and estimate crop production during the harvest. Optical sensors, such as OptRx [58], Cloro-fiLOG [59], Dualex [27], Multiplex [60], Isaria, Crop Circle [61], SPAD-502 [10], Crop-Spec [62], GreenSeeker [63], and ALS-2 N [10], provide crop monitoring under day and night conditions [64], as shown in Table 1. The method of analysis involves collecting a large quantity of recordings per field with many measurements per second [65].…”

Section: Optical Sensorsmentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

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Section: Optical Sensorsmentioning

confidence: 99%

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

et al. 2022

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“…Through the mass flow predicted, it is possible to calculate the yield in a given area (e.g., ton ha −1 ), and together with the coordinates, yield maps can be generated. Yield maps are the key to managing sugarcane spatial variability [ 5 , 6 ]. This is the starting point for implementing Precision Agriculture (PA) practices, which aim to assist decision-making and interventions in high spatial definition in the agricultural fields.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester

Lima

Maldaner

Molin

2021

Sensors

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Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s−1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.

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“…The results showed that more efforts were necessary to modelling mechanistically other significant physiological processes such as nitrogen distribution and uptake, and leaf and tiller senescence. Other researchers (Gao et al, 2020, Canata et al, 2021, Mobe et al, 2021 used some parameters such as weather temperature, soil moisture, etc. to predict barley growth stages.…”

Section: Introductionmentioning

confidence: 99%

Predicting Barley Harvest Time in Dryland Conditions Using Satellite Images

Adiban

Türkoğlu²,

Parchami-Araghi³

2021

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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Barley has an important role in livestock feed. Therefore, an accurate estimation of harvesting time is necessary to minimize the loss in barley farming. The aim of this study is to determine barley harvest time using satellite images accurately. Field data were sampled from the farms in the Dezaj region of the west of Iran. In addition, satellite remote sensing technique was applied during barley growing season in 2019 using Landsat 8 images. The vegetation indexes were used as input in the prediction model in this study. The results showed that satellite imaging has enough potential to predict the harvesting time of barley accurately. R-squared and RMSE values of the best-structured stepwise regression model in this study were 0.791 as well, and 1.34 respectively. This method can be beneficially employed by farm managers to have an accurate estimation of the most appropriate harvesting time and be able to manage the process, which is an important challenge for them.

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Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Cited by 48 publications

References 50 publications

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester

Predicting Barley Harvest Time in Dryland Conditions Using Satellite Images

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