Sensor‐based variable‐rate fungicide application in winter wheat

Tackenberg, Maria; Volkmar, Christa; Dammer, Karl‐Heinz

doi:10.1002/ps.4225

Cited by 23 publications

(21 citation statements)

References 18 publications

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“…One reason could be the higher level of green coverage of 57.6% (average of the 436 camera sensor measurements) in the uniform strip compared with 49.3% (average of the 469 camera sensor measurements) for the camera‐controlled strip at the time of spraying. Because coverage is positively correlated with biomass, this indicates the subsequent higher local yield in the uniform strip. Under practical conditions in field trials it is hard to meet the ceteris paribus principle for testing the influence of a treatment factor on a resulting character .…”

Section: Resultssupporting

confidence: 86%

“…Two foliar diseases Z. tritiici and D. tritici repentis occurred simultaneously in the field trial, although there was no senescence due to drought damage. First testing of the influence of camera‐controlled spraying technology on disease occurrence and crop yield was carried out in 2014 . Because this was a single‐year result, the same experimental question was asked in another 2016 field trial under different field, wheat variety and weather conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Speer) due to reduced air circulation and delayed evaporation of dew. Incorporating disease occurrence into a variable‐rate spraying decision is more complicated, because various parameters interact . Weather, different growing conditions (soil, water and nutrition) and even the microclimate of the crop canopy are known to promote disease…”

Section: Introductionmentioning

confidence: 99%

“…After investigation of the basic principles and a first test of a camera‐controlled spraying technology for variable‐rate fungicide application in cereals, the aim of this research was widescale testing of camera‐controlled variable‐rate spraying technology under different conditions. In total, four field strip trials were performed in 2015 and 2016 to test the new technology under different weather conditions, wheat varieties and disease pressure.…”

Section: Introductionmentioning

confidence: 99%

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Impact of sensor‐controlled variable‐rate fungicide application on yield, senescence and disease occurrence in winter wheat fields

Tackenberg¹,

Volkmar

Schirrmann

et al. 2018

Pest Management Science

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Section: Resultssupporting

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of sensor‐controlled variable‐rate fungicide application on yield, senescence and disease occurrence in winter wheat fields

Tackenberg¹,

Volkmar

Schirrmann

et al. 2018

Pest Management Science

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“…Thousands of images can be nearly automatically processed via ready-to-use software to produce orthoimages or surface models. Because wheat is the crop grown with the highest acreage [3] there is a strong interest in obtaining spatial and temporal information about the wheat canopy in high resolution, e.g., to adapt nitrogen and pesticide application site-specifically to improve production efficiency [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

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Farm Machinery and Processes Management in Sustainable Agriculture

2023

Lecture Notes in Civil Engineering

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EU forestry does not currently have automatic planters, hence the project proposes to develop an innovative technology in which the key role will be played by an autonomous planter designed for establishing forest cultures and afforestation of former farmland and reclaimed areas with the use of seedlings with a covered root system (grown in container nurseries). The device will have a self-levelling traction system, a control system with a satellite navigation module to support autonomous navigation and planting site selection, a mechanism of planting spot preparation, a planting unit, an intelligent robotic arm to feed seedlings from containers to the planting unit according to a given algorithm, a container storage unit with an automated feeder, a drive unit with an electro-hydraulic control system, a control module, and a wireless remote control system. The autonomous planter will significantly reduce the cost and machine inputs during establishing forest cultures and afforestation of former farmland and reclaimed areas with the use of seedlings with a covered root system, and thus perfectly fits into the assumptions of agriculture 4.0.

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Sensor‐based variable‐rate fungicide application in winter wheat

Cited by 23 publications

References 18 publications

Impact of sensor‐controlled variable‐rate fungicide application on yield, senescence and disease occurrence in winter wheat fields

Impact of sensor‐controlled variable‐rate fungicide application on yield, senescence and disease occurrence in winter wheat fields

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Farm Machinery and Processes Management in Sustainable Agriculture

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