An yield estimation in citrus orchards via fruit detection and counting using image processing

Abstract The overall goal of this study is to develop an effective, simple, aptly computer vision algorithm to detect and count citrus on the tree using image processing techniques, to estimate the yield, and to compare the yield estimation results obtained through several methods. This new citrus recognition and counting algorithm was utilized the color features (or schemes) to present an estimate of the citrus yield, and the corresponding models are developed to provide an early estimation of the citrus yield. Citrus images were taken from Jeju, South Korea during daylight and the citrus recognition and counting algorithm were tested on 84 images which were collected from 21 trees. The citrus counting algorithm consisted of the following steps: convert RGB image to HSV, thresholding, orange color detection, noise removal, watershed segmentation, and counting. Distance transform and marker-controlled watershed algorithms were evaluated for automated watershed segmentation in citrus fruits to obtain good result. A correlation coefficient R 2 of 0.93 was obtained between the citrus counting algorithm and counting performed through human observation. The proposed algorithm showed great potential for early prediction of the yield of single citrus trees and the possibility of its uses for further fruit crops.

[1]  Faisal R. Al-Osaimi,et al.  Illumination normalization of facial images by reversing the process of image formation , 2011, Machine Vision and Applications.

[2]  Won Suk Lee,et al.  Trash detection system for a citrus canopy shake and catch harvester using machine vision , 2008 .

[3]  Malrey Lee,et al.  A Novel Technique for tangerine Yield Prediction using Flower Detection Algorithm , 2013, Int. J. Pattern Recognit. Artif. Intell..

[4]  R. Manicka Chezian,et al.  VARIOUS COLOUR SPACES AND COLOUR SPACE CONVERSION ALGORITHMS , 2013 .

[5]  Radnaabazar Chinchuluun.  Citrus Yield Mapping System in Natural Outdoor Scenes using the Watershed Transform , 2006 .

[6]  Tai-hoon Kim,et al.  Grayscale Conversion of Histopathological Slide Images as a Preprocessing Step for Image Segmentation , 2016 .

[7]  Johnny Park,et al.  Segmentation of Apple Fruit from Video via Background Modeling , 2006 .

[8]  Won Suk Lee,et al.  Citrus Yield Mapping System Using Machine Vision , 2003 .

[9]  Dennis Jarvis,et al.  Estimation of mango crop yield using image analysis - Segmentation method , 2013 .

[10]  Salah Sukkarieh,et al.  A Feature Learning Based Approach for Automated Fruit Yield Estimation , 2013, FSR.

[11]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[12]  Jaehoon Choi,et al.  A Novel Red Apple Detection Algorithm Based on AdaBoost Learning , 2015 .

[13]  James Patrick Underwood,et al.  Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry , 2016, Sensors.

[14]  Garrison W. Cottrell,et al.  Color-to-Grayscale: Does the Method Matter in Image Recognition? , 2012, PloS one.

[15]  Long Tang,et al.  Instantaneous Real-Time Kinematic Decimeter-Level Positioning with BeiDou Triple-Frequency Signals over Medium Baselines , 2015, Sensors.