MACHINE VISION-BASED CITRUS YIELD MAPPING SYSTEM

The variability of yield in citrus groves is important for growers to know to make correct management decisions. Cur- rent citrus yield mapping systems require hand harvesting which is labor intensive. In computer vision-based agricultural applications for yield mapping, detecting occluded and non- occluded fruit from acquired images of trees is one of the ma- jor problems. Since there are no completely robust and effi- cient methods, detecting occluded fruit from acquired images has received much attention in computer vision-based agricul- tural applications. This paper presents an automatic machine vision system with two charge coupled device (CCD) cameras, ultrasonic sensors, an encoder and a differential Global Posi- tioning System (GPS) receiver to estimate citrus yield. An alter- native computer vision algorithm was proposed to recognize visible and partially occluded citrus fruit from trees. The aver- age fruit size was determined from images using ultrasonic sensors measuring a distance between the cameras and the fruit laden trees. Finally, a citrus yield map was created to show yield variability for site-specific crop management.