Sweet Citrus Fruit Detection in Thermal Images Using Fuzzy Image Processing

This study proposes a computer vision system based on the capture of thermal images and fuzzy image processing. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. In agriculture, intelligent systems applications have generated great advances in automating some processes in the production chain. To improve the efficiency of those systems is proposes a vision algorithm to estimate the amount of fruits in sweet orange trees. 

The algorithm developed for this project uses the intensification operator to contrast-enhanced and the fuzzy divergence for segmentation and Hough transform for fruit identification. It estimates the numbers of fruits in the tree, a task that is currently manually performed. In order to validate the proposed algorithm a database was created with images of sweet orange acquired in the Maring Farm. 

The validation process indicated that the variation of the tree branch and the fruit temperature is not very high, making it difficult to segment the images using a temperature threshold. Errors in the segmentation algorithm could mean the increase of false positives in the fruit-counting algorithm. Recognition of isolated fruits with the proposed algorithm presented an overall accuracy of 93.5 percent and grouped fruits accuracy was 80 percent. The experiments show the need of other image hardware to improve the recognition of small temperature changes in the image.