Combine Harvest Area Determination by Vector Processing of GPS Position Data

Yield Editor: Software for Removing Errors from Crop Yield Maps

“…Beck et al (2001) noted this issue and suggested that the most practical solution was to avoid recording data with narrower widths. Research into swath width sensing (Reitz and Kutzbach, 1996) and calculation of swath width from GPS data (Han et al, 1997; Drummond et al, 1999) may provide alternative ways of quantifying partial swaths, but such systems are not yet commercially available.…”

“…Currently, no standard method exists for cleaning raw yield data, although many different filtering or screening techniques have been suggested to address specific error types (e.g., Blackmore and Moore, 1999; Drummond et al, 1999; Thylén et al, 2001; Beal and Tian, 2001; Beck et al, 2001; Arslan and Colvin, 2002; Chung et al, 2002; Yang et al, 2002; Simbahan et al, 2004). While many of these techniques could be implemented in a spreadsheet or in simple programming code, some would require a more sophisticated mapping or GIS application.…”

Yield Editor 2.0: Software for Automated Removal of Yield Map Errors

“…Currently, no standard method exists for cleaning raw yield data, although many different filtering or screening techniques have been suggested to address specific error types (e.g., Han et al, 1997;Blackmore and Moore, 1999;Drummond et al, 1999;Thylén et al, 2000;Beal and Tian, 2001;Beck et al, 2001;Arslan and Colvin, 2002;Chung et al, 2002;Yang et al, 2002;Simbahan et al, 2004;Lee et al, 2012). While many of these techniques could be implemented in a spreadsheet or in simple programming code, some would require a more sophisticated mapping or GIS application.…”

Trash detection system for a citrus canopy shake and catch harvester using machine vision

“…Sunflower yield mapping using multi-spectral imaging and a neural network classifier (Gutiérrez et al, 2008), detection and classification of Phalaenops seedling diseases using a combination of color and texture (Huang, 2007), identification of diseases in citrus using a combination of color and texture (Pydipati et al, 2006) are among the many studies conducted to move toward intelligent farming using machine vision. Drummond et al (1999) have used a vector method to combine GPS data into a geographic information system and use it as a measure of harvested time per unit area. Dettmar et al (1996) have demonstrated the use of neural networks to classify orange juice based products.…”