Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges : colour segmentation based on linear discriminant analysis , contour curvature analysis , and a thinning process which involves iterating until the stem becomes a skeleton . These techniques are able to determine the presence or absence of a stem with certainty , to locate the stems from random views with more than 90% accuracy and from profile images with an accuracy ranging from 92 и 4% to 100% depending on the method used . Finally , determination of the length and cutting point of the stem is achieved with only 3 и 8% of failures .÷ 1996 Silsoe Research Institute . IntroductionMechanical harvesting of citrus fruits brings some additional problems that have not been present in manual harvesting , such as the presence of fruits with long stems , with leaves or without calyx after detachment from the tree . Long stems and leaves can cause damage on adjacent fruits , while the absence of calyx opens a way for possible infections during transport and storage . This also means a loss of uniformity of the product which is not desirable for the fresh market . Therefore , a system for cutting long stems and for detecting the absence of calyx before the fruit arrives at the packing houses would be advantageous .Mechanical destemming systems , such as the one reported by Chen , 1 are usually based on random rotation of the oranges against cutting surfaces . However , the contact with these surfaces may cause some damage to the fruits . Some The random orientation of fruit on conveyor belts is often a problem in stem cutting systems . The sphericity of oranges hinders the possibility of mechanical orientation , so the detection of the stem -calyx area by a camera seems to be an adequate way to orientate the fruit . Once the stem has been situated at the correct position , it can be characterized and measured so the decision to cut the stem of f or not can be made .A system for orientation of oranges would be of interest for implementation on the CITRUS robot 6 in order to cut the stem of f after the picking operation .The objectives of this study were to design reliable image analysis methods (1) to locate , using colour vision , the stem -calyx area of oranges randomly presented to a camera , in order to be able to orientate the fruit , as well as to classify it on the basis of presence or absence of stem and leaves , and (2) to study the profiles of the fruit previously oriented and locate the stem to determine its length and cutting point . . Materials and methodsColour images were acquired with a charge coupled Since dif fuse light is highly ef fective in eliminating shadows and specular reflection , and in preserving well-defined edges , an illumination chamber with indirect fluorescent light and dif fusing material was built and used to take colour images , while illumination by contrast was employed to acquire profile images .The following three working methods were defi...

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The best alternative for reducing citrus production costs is mechanization. Machine vision is a reliable technology for the automatic inspection of fresh fruits and vegetables that can be adapted to harvesting machines. In these, fruits can be inspected before sending them to the packinghouse and machine vision provides important information for subsequent processing and avoids spending further resources in non-marketable fruit. The present work describes a computer vision system installed on a harvesting machine developed jointly by IVIA and a Spanish enterprise. In this machine, hand pickers directly drop the fruit as they collect it, which results in an important increase of productivity. The machine vision system is placed over rollers in order to inspect the produce, and separate those that can be directly sent to the fresh market from those that do not meet minimal quality requirements but can be used by the processing industry, based on color, size and the presence of surface damages. The system was tested under field conditions.

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