Variable rate spraying application on cotton using an electronic flow controller

Baio, Fábio Henrique Rojo; Neves, Danilo Carvalho; Souza, Heloisa Bueno; Leal, Aguinaldo José Freitas; Leite, R. C.; Molin, José Paulo; Silva, S. P.

doi:10.1007/s11119-018-9564-7

Cited by 16 publications

(14 citation statements)

References 16 publications

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“…Although the algorithms were calibrated to have an average rate similar to the fixed application rate, the savings were mainly to the areas not applied, due to crop vigor below the minimum threshold defined to each application. The results obtained by other authors in similar conditions [14] were more conclusive in showing the effect of reduced plant height variability between application zones using VRA with an electronic flow controller in the same machine model used in this study. The authors also found a reduction in the coefficient of variation of the percentage of opened bolls when using the VRA of the fruit ripener between the application zone classes.…”

Section: Input Savings and Yield Response To Variable Rate Of Pgrsupporting

confidence: 82%

“…Besides interfering positively with mechanized harvesting procedure, the plant height control provides benefits regarding the harvested cotton, particularly the fiber quality [14]. Fiber quality parameters were not measured in this work, but some field observations confirmed this statement.…”

Section: Input Savings and Yield Response To Variable Rate Of Pgrmentioning

confidence: 67%

“…The methodology used is similar to what was presented in [14], but instead of dividing the field into a few application zones, regression analysis was used to predict optimal PGR rate for every pixel, allowing rates to vary between 80 and 120% of the field average rate for each application. When the optimal rate was less than 50% of field average, the rate was set to zero.…”

Section: Variable Rate Application Of Pgrmentioning

confidence: 99%

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Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors

et al. 2018

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Abstract:Factors affecting cotton development present spatial and temporal variability. Plant growth regulators (PGR) are used to control vegetative growth, promote higher yields, better fiber quality, and facilitate mechanical harvest. The optimal rate of PGR application depends on crop height, biomass, and growth rate. Thus, the objective of this study was to evaluate optical and ultrasonic crop canopy sensors to detect the crop spatial variability in cotton fields, and to develop strategies for using this information to perform variable rate application (VRA) of PGR in cotton. Field trials were conducted in Midwest Brazil during the 2013/2014 and 2014/2015 crop seasons. Two optical and two ultrasonic active crop canopy sensors were evaluated as tools to detect crop variability. On-farm trials were used to develop and validate algorithms for VRA based on within-field variations in crop response to PGR applications. The overall performance of the sensors to predict crop height and the accumulation of biomass in cotton was satisfactory. Short distance variability was predominant in some fields, reducing the performance of the sensors while making current technology for variable rate application of PGR inadequate. In areas with large scale variability, the VRA led to 17% savings in PGR products and no significant effect on yield was observed. Ultrasonic sensors present can be a low-cost alternative to implement variable rate application of PGR in real time.

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Section: Input Savings and Yield Response To Variable Rate Of Pgrsupporting

confidence: 82%

Section: Input Savings and Yield Response To Variable Rate Of Pgrmentioning

confidence: 67%

Section: Variable Rate Application Of Pgrmentioning

confidence: 99%

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Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors

et al. 2018

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“…Similarly, both the farmers and the custom service providers are becoming increasingly interested in quantifying (mapping) the site-specific grain yield. However, the machine Agriculture 2021, 11, 897 2 of 17 hiring rates are increased when a commercially available grain meter is used, and it is believed to affect the economic attractiveness of the hired machine [13,14]. National statistics suggest a considerable increase in custom hiring rates of rice combines in Thailand, mounting to 164.42 USD ha −1 recently (2019), whereas, it was 108.51 USD ha −1 in 2015, 94.13 USD ha −1 in 2010, 76.05 USD ha −1 in 2005, and 61.65 USD ha −1 in 2000 [11].…”

Section: Introductionmentioning

confidence: 99%

A Grain Yield Sensor for Yield Mapping with Local Rice Combine Harvester

et al. 2021

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Rice grain yield was estimated from a locally made Thai combine harvester using a specially developed sensing and monitoring system. The yield monitoring and sensing system, mounted on the rice combine harvester, collected and logged grain mass flow rate and moisture content, as well as pertinent information related to field, position and navigation. The developed system comprised a yield meter, GNSS receiver and a computer installed with customized software, which, when assembled on a local rice combine, mapped real-time rice yield along with grain moisture content. The performance of the developed system was evaluated at three neighboring (identically managed) rice fields. ArcGIS® software was used to create grain yield map with geographical information of the fields. The average grain yield values recorded were 3.63, 3.84 and 3.60 t ha−1, and grain moisture contents (w.b.) were 22.42%, 23.50% and 24.71% from the three fields, respectively. Overall average grain yield was 3.84 t ha−1 (CV = 63.68%) with 578.10 and 7761.58 kg ha−1 as the minimum and maximum values, respectively. The coefficients of variation in grain yield of the three fields were 57.44%, 63.68% and 60.41%, respectively. The system performance was evaluated at four different cutter bar heights (0.18, 0.25, 0.35 and 0.40 m) during the test. As expected, the tallest cutter bar height (0.40 m) offered the least error of 12.50% in yield estimation. The results confirmed that the developed grain yield sensor could be successfully used with the local rice combine harvester; hence, offers and ‘up-gradation’ potential in Thai agricultural mechanization.

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“…Pflanz et al [8] could discriminate between wheat and weeds with 98% accuracy and differentiate various weed species with 87% accuracy. Thus, weed mapping is a major issue to provide decision-making support for variable rate spraying application [28,29]. However, due to the reduction of chemical inputs and to climate change, particular attention should be given to a regular and precise monitoring of crop growth [30,31].…”

Section: Introductionmentioning

confidence: 99%

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

Gée

Denimal

2020

Remote Sensing

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In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

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Variable rate spraying application on cotton using an electronic flow controller

Cited by 16 publications

References 16 publications

Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors

Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors

A Grain Yield Sensor for Yield Mapping with Local Rice Combine Harvester

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

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