Predicting Yield of Fruit and Flowers using Digital Image Analysis

Background/Objectives: The objective of this research is to predict the yield of fruit and flowers to help farmers to plan the sales, the shipment and operations related to the harvest. Methods/Statistical Analysis: The proposed algorithm involves noise removal, image segmentation, size thresholding and shape analysis; for automated counting of the regions of interest, and finally yield prediction. We have used different channels of two color spaces RGB and YCbCr for our study. 28 images of Dragon fruit and 26 images of Daisy flower are used for simulations. Findings: The percentage error in automated counting for RGB model (R-G channel) is 8.75% for Dragon fruit and 11.30% for Daisy flower while for YCbCr model (Cr channel) percentage error is 8.07% for Dragon fruit and 5.54% for Daisy flower. Based on our analysis we may conclude that Cr channel of YCbCr color model gives better results. Regression analysis gives R2 equal to 0.9517 and 0.9751 for Dragon fruit and Daisy flower respectively between the manual and automated counting. The average percentage error in yield prediction for Dragon fruit is 1.40% and Daisy flower is 5.52%. Application/Improvement: Based on our findings we can conclude that image processing based automated system for agricultural yield prediction can help to estimate the agricultural harvest.