New apple fruit recognition algorithms based on colour features are presented to estimate the number of fruits and develop models for early prediction of apple yield, in a multi-disciplinary approach linking computer science with agricultural engineering and horticulture as part of precision agriculture. Fifty cv. ‘Gala’ apple digital images were captured twice, i.e. after June drop and during ripening, on the preferred western side of the tree row with a variability of between 70 and 170 fruit per tree, under natural daylight conditions at Bonn, Germany. Several image processing algorithms and fruit counting algorithms were used to analyse the apple images. Finally, an apple recognition algorithm with colour difference R − B (red minus blue) and G − R (green minus red) was developed for apple images after June drop, and two different colour models were used to segment ripening period apple images. The algorithm was tested on 50 images of trees in each period. Close correlation coefficients R 2 of 0.80 and 0.85 were obtained for two developmental periods between apples detected by the fruit counting algorithm and those manually counted. Two sets of data in each period were used for modelling yield prediction of the apple fruits. In the calibration data set, the R 2 values between apples detected by the fruit counting algorithm and actual harvested yield were from 0.57 for young fruit after June drop to 0.70 in the fruit ripening period. In the validation data set, the R 2 value between the number of apples predicted by the model and actual yield at harvest ranged from 0.58 to 0.71. The proposed model showed great potential for early prediction of yield for individual trees of apple and possibly other fruit crops.
Precision Agriculture – Springer Journals
Published: Jun 9, 2012
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera