Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester

Domsch, Horst; Heisig, Michael; Witzke, K.

doi:10.1007/s11119-008-9076-y

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…The system showed an R 2 of 0.90 when correlated to a manual stand count. Domsch et al (2008) used RGB aerial imagery taken at a growing stage to estimate crop yield at harvesting time. When the researchers used VARI (introduced by Stark et al (2000)) and the data for a selected few tracks, VARI versus measured yield showed linear patterns for rye fields (R 2 :0.40 and 0.75) and power patterns for winter barley fields (R 2 :0.76 and 0.86).…”

Section: Other Methodsmentioning

confidence: 99%

Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review

Chung

Choi

Lee

et al. 2016

Journal of Biosystems Engineering

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Purpose: Yield monitoring systems are an essential component of precision agriculture. They indicate the spatial variability of crop yield in fields, and have become an important factor in modern harvesters. The objective of this paper was to review research trends related to yield monitoring sensors for grain crops. Methods: The literature was reviewed for research on the major sensing components of grain yield monitoring systems. These major components included grain flow sensors, moisture content sensors, and cutting width sensors. Sensors were classified by sensing principle and type, and their performance was also reviewed. Results: The main targeted harvesting grain crops were rice, wheat, corn, barley, and grain sorghum. Grain flow sensors were classified into mass flow and volume flow methods. Mass flow sensors were mounted primarily at the clean grain elevator head or under the grain tank, and volume flow sensors were mounted at the head or in the middle of the elevator. Mass flow methods used weighing, force impact, and radiometric approaches, some of which resulted in measurement error levels lower than 5% (R 2 = 0.99). Volume flow methods included paddle wheel type and optical type, and in the best cases produced error levels lower than 3%. Grain moisture content sensing was in many cases achieved using capacitive modules. In some cases, errors were lower than 1%. Cutting width was measured by ultrasonic distance sensors mounted at both sides of the header dividers, and the errors were in some cases lower than 5%. Conclusions: The design and fabrication of an integrated yield monitoring system for a target crop would be affected by the selection of a sensing approach, as well as the layout and mounting of the sensors. For accurate estimation of yield, signal processing and correction measures should be also implemented.

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Section: Other Methodsmentioning

confidence: 99%

Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review

Chung

Choi

Lee

et al. 2016

Journal of Biosystems Engineering

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“…Since Super-rice was chosen as the test object, it was hoped that the broken white and whole yellow kernels in the visual window could reflect more light and be more clearly "seen" and "classified" by the camera [14,15] . In addition, the light distribution was supposed to be homogeneous to avoid local over the brightness of the image caused by the reflection highlights of transparent visual windows [16][17][18][19] . Therefore, it is necessary to optimize the angle and distance of the surface light source illuminating at the visual window [20,21] .…”

Section: Light Sourcementioning

confidence: 99%

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Chen

Liao

Zou

et al. 2020

International Journal of Agricultural and Biological Engineering

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Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester. It is affected by operating parameters of a combine such as feeding rate, the peripheral speed of the threshing cylinder and concave clearance, and shows complex non-linear law. Real-time acquisition of the breakage rate is an effective way to find the correlation of them. In addition, real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard. In this study, a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed. The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process. According to the working characteristics of the combine, the illumination and installation of the light source were optimized, and the lateral lighting system was constructed. A two-step method of "color training-verification" was applied to identify the whole and broken kernels. In the first step, the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation, extract the color spectrum of each particle in color-space HSL and output the recognition model file. The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory. The experiments of about 2300 particles showed that the recognition accuracy of 96% was attained, and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency. The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.

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“…However, when UAS derived VIs have been used, especially in small plots and comparing varieties or treatments, other VIs have appeared as good predictors. For example, commonly selected UAS derived VIs that have been good predictors, aside from NDVI [ 22 , 31 ], include Green-Normalized Difference Vegetation Index (GNDVI) [ 2 , 11 , 22 , 31 , 32 ], Visible Atmospherically Resistant Index (VARI) [ 33 – 35 ], and Normalized Difference Red Edge (NDRE) [ 11 , 22 , 36 ]. Varied modeling procedures for incorporating and evaluating different VIs in yield prediction have been reported, including hierarchical linear regression [ 29 ], multiple linear regression [ 22 , 33 ], multivariate regression modeling with other phenological matrices [ 21 ], random forest [ 11 , 32 , 37 ], support vector machine [ 32 ], and boosted regression tree [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Chatterjee

Adak²,

Wilde³

et al. 2023

PLoS ONE

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Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize (Zea mays L.) were evaluated in two environment trials–one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68–72%), but inconsistent models. A little sacrifice in accuracy (~60–65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5–10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

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Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester

Cited by 9 publications

References 26 publications

Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review

Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

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