Filip Feyaerts

Multi-spectral vision system for weed detection

Abstract The proposed online system distinguishes crop from weeds based on multi-spectal reflectance gathered with an imaging spectrograph. Under field conditions, up to 86% of the vegetation samples (80% of crop, 91% of weed) were recognized herbicide reductions of up to 90%.

Hyperspectral image sensor for weed-selective spraying

Recognizing, online, cops and weeds enables to reduce the use of chemicals in agriculture. First, a sensor and classifier is proposed to measure and classify, online, the plant reflectance. However, as plant reflectance varies with unknown field dependent plant stress factors, the classifier must be trained on each field separately in order to recognize crop and weeds accurately on that field. Collecting the samples manually requires user-knowledge and time and is therefore economically not feasible. The posed tree-based cluster algorithm enables to automatically collect and label the necessary set of training samples for crops that are planted in rows, thus eliminating every user- interaction and user-knowledge. The classifier, trained with the automatically collected and labeled training samples, is able to recognize crop and weeds with an accuracy of almost 94 percent. This result in acceptable weed hit rates and significant herbicide reductions. Spot-spraying on the weeds only becomes economically feasible.

Using shape to correct for observed nonuniform color in automated egg grading

We report on algorithmic aspects for the automated visual quality control for grading of brown eggs. Using RGB color images of four different views of every egg enabled to analyze the entire eggshell. The scene was illuminated using a set of white fluorescent tubes placed in a rectangular grid. After detection and approximation of the egg contour (ellipse fitted), the color was corrected to compensate for the elliptical shape of the eggs. A second order polynomial was fitted through points taken from subsequent horizontal lines inside the egg. Iteration was used to reject outliers (most likely points with visual defects). The shape- corrected intensity was calculated as the signed difference between polynomial and measured value, increased with the average egg intensity. Based on the corrected color, dirt regions like yolk, manure, blood, and red mite spots were segmented from the egg-background. Features based on color and shapes were calculated for every segmented region as the combined space and color moments of zeroth, first and second order. A classifier identified most of the defective eggs. Elimination of false rejects due to mirror reflection of the light tubes on some eggs (segmented because of the different color) is currently under investigation.