Presisjonstiltak mot ugras

Ugras er ofte ujevnt fordelt i åkeren. Tiltak for å fjerne ugraset avgrenses til areal med uakseptabel ugrasmengde. Slike presisjonstiltak mot ugras er mulige når sensorteknologi og agronomisk kunnskap settes sammen.

Presisjonstiltak mot ugras kan deles inn i tre hovedtrinn:  1. Gjenkjenning og kartlegging av ugras (og/eller nytteplante) 2. Beslutningsmodell (f.eks. ugrasmengde som ikke gir avlingsnedgang) 3. Presisjonstiltak (f.eks. ingen eller full dose av frøugrasmiddel)
Presisjonstiltak mot ugras kan deles inn i tre hovedtrinn: 1. Gjenkjenning og kartlegging av ugras (og/eller nytteplante) 2. Beslutningsmodell (f.eks. ugrasmengde som ikke gir avlingsnedgang) 3. Presisjonstiltak (f.eks. ingen eller full dose av frøugrasmiddel) Foto: Therese W. Berge, Illustrasjoner: Roar Lågbu
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Tjenester

Korsmos ugrashage

Korsmos ugrashage gir digital tilgang til et utvalg av ’Korsmos ugrasplansjer’, supplert med nye fotografier av ugrasarter.

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Plantevernguiden

Plantevernguiden er en nettbasert tjeneste som gir deg en samlet oversikt over godkjente kjemiske og biologiske plantevernmidler. Tjenesten er utviklet i et samarbeid mellom Mattilsynet og NIBIO.

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Publikasjoner

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Sammendrag

Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes systematic growing techniques to navigate and operate in the field. By the use of machine vision it separates seeded vegetable crops from weed. Each weed within the row is treated with individual herbicide droplets, without affecting the crop. This results in a significant reduction in herbicide use, and allows for the use of herbicides that would otherwise harm the crop. The robot is tailored to this purpose with cost, maintainability, efficient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benefit of reduced weight, complexity and cost. Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 μg glyphosate or 0.15 μg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for droplet stability and leaf retention properties. The common herbicide glyphosate had no effect unless mixed with suitable additives. A field trial with the robot was performed in a carrot field, and all the weeds were effectively controlled with the DoD system applying 5.3 μg of glyphosate per droplet. The robot and DoD system represent a paradigm shift to the environmental impact and health risks of weed control, while providing a valuable tool to the producers.

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Sammendrag

The success of precision agriculture relies largely on our ability to identify how the plants’ growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11–20 DAS. Differentiation of more than one stress factors with the same index was difficult.

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Sammendrag

Arable weeds are generally distributed in patches, while herbicides are applied uniformly. Herbicides can be saved if only the patches are sprayed, i.e. patch spraying (PS). Bottlenecks for cost-effective PS are weed monitoring technology and valid technology-based decision rules for PS (thresholds). The novel machine vision algorithm Weedcer has been developed as an efficient weed monitoring tool for PS. Weedcer estimates the proportions of young weed leaves and cereal leaves in high resolution red–green–blue images. We conducted field trials to test relative weed cover (RWC) and relative mayweed cover (RMC) estimated by Weedcer as decision rules for PS. RWC is the total weed cover divided by the total plant cover and RMC is the mayweed cover divided by the total plant cover. The main criterion for evaluation and basis of these thresholds was the measured grain yield. Images (about 0.06-m2) were acquired with a GPS guided autonomous field robot in spring, the normal time for spraying seed-propagated broadleaf weeds in both winter – and spring cereals in Norway. Three map-based trials (weed monitoring and spraying in two separate operations) showed that mean RWC per management unit (12.0 × 12.5-m) was generally adequate. In winter wheat heavily infested with scentless mayweed (Tripleurospermum inodorum (L.) Sch.Bip.) and/or scented mayweed (Matricaria recutita L.), the mean RMC per management unit was more adequate. Progress during the project allowed three additional trials conducted in real-time (weed monitoring and spraying in the same operation). These were conducted with the robot in spring cereals, and showed that a weighted moving average of RWC per image was adequate. The sprayed and unsprayed management units in these trials were minimum 3.0 × 3.0-m and 0.5 × 3.0-m, respectively. Results indicated that the Weedcer-based thresholds should be lower in wheat (Triticum aestivum) than in barley (Hordeum vulgare).

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Sammendrag

Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm ("WeedFinder") estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red-green-blue images. The ability of "WeedFinder" to predict 'spray'/'no spray' decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, "WeedFinder" gave correct spray decisions in 65-85% of the test images. With discriminant analysis, corresponding mean rates were 84-90%. Future versions of "WeedFinder" must be more accurate and accommodate weed species recognition.

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Sammendrag

A possible cost-effective real-time patch spraying implementation against seed-propagated broad-leaved weeds in cereals is a camera mounted in front of the tractor taking images at feasible distances in the direction of travel, on-board image analysis software and entire boom switched on and off. To assess this implementation, manual weed counts (0.25 m(2) quadrats) in a 1.5 m x 2 m grid, were used to simulate camera outputs. Each quadrat was classified into 'spray' and 'not spray' decisions based on a threshold model, and the resulting map defined the 'ground truth'. Subsequently, 'on/off' spraying at larger control areas where sizes were given by the boom width and image distance, and spraying decision controlled by weed status at the single quadrat simulating the camera's view, were simulated. These coarser maps were compared with 'ground truth', to estimate mapping error (area above threshold not sprayed), spraying error (area below threshold sprayed), total error (sum of mapping and spraying error) and the herbicide reduction. Three levels of the threshold model were tested. Results were used to fit models that predict errors from boom width and image distance. Size of control area did not on average affect the magnitude of the simulated herbicide reductions, but the bigger the control area the higher the risk that the simulated herbicide reduction deviate from the reduction in 'ground truth'. Mean simulated herbicide reductions were 42-59%, depending on threshold level. Only minor differences due to threshold level were seen for mean mapping and spraying errors at given spraying resolutions. Using original threshold level and image distance 2 m, predicted total errors for boom widths 2 m, 6 m, 20 m and 40 m would be 6%, 10%, 16% and 17%, respectively. Results indicate that control area should not exceed about 10 m 2 if acceptable total error is maximum 10%.

Sammendrag

A major obstacle to patch spraying of broad-leaved weeds in cereals is a cost-effective method to assess within-field heterogeneity of the weed population. One method could be a camera mounted in front of the spraying vehicle, online image analysis, and field sprayer shifting between "on" and "off" as the predefined weed damage threshold level is reached. Because such a camera will capture a very limited area (

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