La vision artificielle est une méthode d'avenir pour la reconnaissance automatisée des plantes adventices

Abstract

Machine vision is a method for the future to automatically recognize weeds from plants. Weeds compete with crop plants for sunlight, moisture and nutrients and can have a detrimental impact on crop yields and quality if uncontrolled. They are destroyed by chemical, non-chemical and integrated methods. To perform a site-specific weeds destruction, combination of these techniques with ground-based machine vision technology has high potential. Several methods exist to differentiate weeds from soil, between the rows. The more complicated problem is encountered when weeds are mixed to crops within the rows. Algorithms based on colorimetric or shape features are widely dependent on the variability of weeds and crops and are difficult to transpose from one situation to another. Measurement of plant height is a promising method, since at low spatial scale, the growing speed is more uniform for the plants than for the weeds. This growing speed is function of the height and of a characteristic time, such as the number of days after sowing. To implement this method, active stereoscopy combined to an accurate measurement of the soil microrelief is required.

Keywords

• Weeds
• image analysis
• reflectance
• vegetal morphology
• stereoscopy