Weed Detection Using Canopy Reflection

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.     

Yield variability related to landscape properties of a loamy soil in central Belgium

A field was investigated with precision farming techniques to delineate zones with different yield potential due to previous soil erosion. Winter wheat (Triticum aestivum L.) grain yield, straw yield, biomass and harvest index were measured with a combine harvester. Fuzzy clustering of grain and straw yield provided good delineation of zones in the field with different yield potential. Entropy and fuzziness calculations for different number of classes resulted in a division of the field into five clearly differentiated yield potential zones. Straw yield, biomass and harvest index were significantly different among zones. Grain yield was significantly different for all zones, except for two. Elevation and slope of the field were measured from a global positioning system (GPS) unit on the combine. Both were related to yield variability in the field. Average elevation, slope and soil type were calculated per cluster class. High grain yield, straw yield and biomass could be related to flat, high places in the field with little erosion. Good grain yield, low straw yield and high harvest index were found on relatively steep slopes subjected to erosion. High straw yield and low grain yield were found at low places in the field on relatively steep slopes. Lowest grain yield, straw yield and biomass were located on steepest slopes with high erosion and in depressions where accumulation of eroded soil took place and slumping and crusting of the soil were present. This information suggests that variable management on a site-specific basis would optimize yield and inputs.     

Effects of bruise type on discrimination of bruised and non-bruised ‘Golden Delicious’ apples by VIS/NIR spectroscopy

Visual (VIS) and near-infrared (NIR) spectroscopy was applied to discriminate bruises and non-bruised healthy spots on ‘Golden Delicious’ apples. Two types of bruises were examined; those created by controlled impact and those by compression. Reflectance spectra of apples were measured in the range from 400 to 1700 nm. The data were analysed with canonical discriminant analysis (CDA). The squared canonical correlation (CR²) was 0.74 for discriminating impact bruises and non-bruised tissue, and a CR² of 0.68 was obtained for distinguishing compression bruises and sound tissue. Based on the linear discriminant functions, built with canonical components, the misclassification errors for non-bruised apples were mainly due to the presence of compression bruises. The classification accuracy was improved by taking the type of bruises into account in the model.