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Article

Smart Harrowing—Adjusting the Treatment Intensity Based on Machine Vision to Achieve a Uniform Weed Control Selectivity under Heterogeneous Field Conditions

Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany
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Author to whom correspondence should be addressed.
Agronomy 2020, 10(12), 1925; https://doi.org/10.3390/agronomy10121925
Submission received: 6 November 2020 / Revised: 2 December 2020 / Accepted: 4 December 2020 / Published: 8 December 2020
(This article belongs to the Special Issue Crop Monitoring and Weed Management Based on Sensor-Actuation Systems)

Abstract

Harrowing is mostly applied with a constant intensity across the whole field. Heterogeneous field conditions such as variable soil texture, different crop growth stages, variations of the weed infestation level, and weed species composition are usually not considered during the treatment. This study offers a new approach to sensor-based harrowing which addresses these field variations. Smart harrowing requires the continuous adaptation of the treatment intensity to maintain the same level of crop selectivity while ensuring a high weed control efficacy. Therefore, a harrow was equipped with a sensor-system to automatically adjust the angle of the harrow tines based on a newly developed decision algorithm. In 2020, three field experiments were conducted in winter wheat and spring oats to investigate the response of the weed control efficacy and the crop to different harrowing intensities, in Southwest Germany. In all experiments, six levels of crop soil cover (CSC) were tested. The CSC determines the balance between crop damage and weed removal. Each experiment contained an untreated control and an herbicide treatment as a comparison to the harrowing treatments. The results showed an increase in the weed control efficacy (WCE) with an increasing CSC threshold. Difficult-to-control weed species such as Cirsium arvense L. and Galium aparine L. were best controlled with a CSC threshold of 70%. However, 70% CSC caused up to 50% crop biomass loss and up to 2 t·ha−1 of grain yield reduction. With a CSC threshold of 20% it was possible to control up to 98% of Thlaspi arvense L. The highest crop biomass, grain yield, and selectivity were achieved with an CSC threshold of 20–25% at all locations. With this harrowing intensity, grain yields were higher than in the herbicide plots and a WCE of 68–98% was achieved. Due to the rapid adjustment of tine angle, the new sensor-based harrow allows users to apply the most selective harrowing intensity in every location of the field. Therefore, it can achieve equal weed control efficacies as using herbicide applications.
Keywords: digital farming; digital image analysis; mechanical weeding; precision farming digital farming; digital image analysis; mechanical weeding; precision farming

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MDPI and ACS Style

Spaeth, M.; Machleb, J.; Peteinatos, G.G.; Saile, M.; Gerhards, R. Smart Harrowing—Adjusting the Treatment Intensity Based on Machine Vision to Achieve a Uniform Weed Control Selectivity under Heterogeneous Field Conditions. Agronomy 2020, 10, 1925. https://doi.org/10.3390/agronomy10121925

AMA Style

Spaeth M, Machleb J, Peteinatos GG, Saile M, Gerhards R. Smart Harrowing—Adjusting the Treatment Intensity Based on Machine Vision to Achieve a Uniform Weed Control Selectivity under Heterogeneous Field Conditions. Agronomy. 2020; 10(12):1925. https://doi.org/10.3390/agronomy10121925

Chicago/Turabian Style

Spaeth, Michael, Jannis Machleb, Gerassimos G. Peteinatos, Marcus Saile, and Roland Gerhards. 2020. "Smart Harrowing—Adjusting the Treatment Intensity Based on Machine Vision to Achieve a Uniform Weed Control Selectivity under Heterogeneous Field Conditions" Agronomy 10, no. 12: 1925. https://doi.org/10.3390/agronomy10121925

APA Style

Spaeth, M., Machleb, J., Peteinatos, G. G., Saile, M., & Gerhards, R. (2020). Smart Harrowing—Adjusting the Treatment Intensity Based on Machine Vision to Achieve a Uniform Weed Control Selectivity under Heterogeneous Field Conditions. Agronomy, 10(12), 1925. https://doi.org/10.3390/agronomy10121925

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