Evaluating spatial within plot crop variability for different management practices with an optical sensor?

Govaerts, Bram; Verhulst, Nele; Sayre, Ken D.; Corte, Pieter De; Goudeseune, Bart; Lichter, Kelly; Crossa, J.; Deckers, Jozef; Dendooven, Luc

doi:10.1007/s11104-007-9358-6

Cited by 35 publications

(32 citation statements)

References 32 publications

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“…In general, this tool has been used to determine the amount of vegetation coverage related to crop canopies without distinguishing its location (Govaerts et al, 2007;Verhulst et al, 2009). In the soybean Reflectance indices as adiagnostic tool for weed control ... experiment, the narrow row spacing was identified as a limiting factor for assessment of weed presence with the GreenSeeker TM tool.…”

Section: Resultsmentioning

confidence: 99%

“…The conditions for using the GreenSeeker™ tool and for achieving the digital photography were optimized in relation to the weed canopy, the horizontal location of the reading, walking speed, and the number of readings per sample area. These determinations were based on studies using the GreenSeeker TM and WeedSeeker sensors (Sui et al, 2008, Grohs et al, 2009) and digital photography (Gerhards & Christensen, 2003;Rasmussen et al, 2007).…”

Section: Resultsmentioning

confidence: 99%

“…Reflectance indices as adiagnostic tool for weed control ... according to procedures described in Verhulst et al, (2009) andGrohs et al, (2009). The NDVI consists of the ratio of the (NIR-Red)/(NIR+Red).…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

“…The generated radiation is reflected and measured by a photodiode. The GreenSeeker TM equipment was originally developed for crop canopy quantification and has been validated and used for over the top nitrogen application at variable rates in several crops (Grohs et al, 2009;Rambo et al, 2010). The normalized differential vegetation index (NDVI) is the most commonly used parameter for crop nitrogen prediction, quantification of various plant species and other uses in precision agriculture (Sui et al, 2008;Verhuls et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The normalized differential vegetation index (NDVI) is the most commonly used parameter for crop nitrogen prediction, quantification of various plant species and other uses in precision agriculture (Sui et al, 2008;Verhuls et al, 2009). Larger numerical values of NDVI are associated with greater crop vigour and greater amounts of aboveground biomass (Grohs et al, 2009). It is important to note the existence of similar equipment, such as the Weedseeker™ (Sui et al, 2008) and an optoelectronic weed sensor (Dammer & Wartenberg, 2007), Reflectance indices as adiagnostic tool for weed control ... which were developed specifically for the quantification of weeds.…”

Section: Introductionmentioning

confidence: 99%

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Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

et al. 2012

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Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Data Acquisition and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

et al. 2012

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In‐season potato yield prediction with active optical sensors

Zaeen

Sharma

Jasim

et al. 2020

Agrosystems Geosci & Env

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Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha−1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.

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Plant‐to‐plant biomass and yield variability in corn–soybean rotations under three tillage regimes

O’Brien

Hatfield

2021

Agronomy Journal

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Agronomic management practices have the potential to affect plant‐to‐plant variability of biomass accumulation and grain production. However, the relationship between the variability of crop production per plant (i.e., grain weight) and crop production per unit area (i.e., grain yield) is inconsistent because high variability in grain weight has been linked with lower yields in some instances but not others. The objective of this experiment was to investigate plant‐to‐plant variability of biomass accumulation and grain production at three developmental stages in corn (Zea mays L.)–soybean (Glycine max [Merr.] L.) rotations under conventional tillage, strip tillage, and no tillage. Plant‐to‐plant biomass variability, as measured by the coefficient of variation (CV) of crop biomass at three phenological stages, decreased throughout the growing season in corn but increased in soybean; these trends did not differ among tillage systems but did change among the 4 yr of the experiment. The CV of grain weight per corn plant was higher in no‐till for two of the study years, suggesting an interaction between tillage and weather patterns that affects variability. Overall, mean grain weight per plant increased as CV of grain weight decreased in corn but not in soybean. This finding suggests that corn yields were maximized when all individual plants were producing relatively similar amounts of grain, whereas this relationship was not present in soybean. The findings of this study indicate that tillage practices do not consistently affect plant‐to‐plant variability of biomass or grain production and must be understood in context of weather conditions and other agronomic management practices.

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Evaluating spatial within plot crop variability for different management practices with an optical sensor?

Cited by 35 publications

References 32 publications

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

In‐season potato yield prediction with active optical sensors

Plant‐to‐plant biomass and yield variability in corn–soybean rotations under three tillage regimes

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