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The journal AGRICULTURA (A) publishes scientific works from the following fields: animal science, plant production, farm mechanisation, land management, agricultural economics, ecology, biotechnology, microbiology
ISSN 1581-5439
Home Issues Issue 3 Application of image analysis for monitoring growth

Application of image analysis for monitoring growth

Denis STAJNKO and Miran LAKOTA
pp. 6-11

A new approach for counting apple fruits, measuring fruit’s diameter and estimating the current yield under flash lighting conditions in the fruit tree plantation was developed and tested in the 2002 and 2003. During the vegetation images of ten trees were captured five times in both years by applying CCD camera. A close correlation was established between manually counted number of fruits per tree and the estimated number of fruits (r=0.70 to 0.88). However, relatively lower coefficient was estimated for measuring the fruit’s diameter (r=0.33 to 0.88). The established correlation coefficients for the average yield per tree was also increasing with the ripening of fruit significantly (r=0.28 to 0.87), therefore the developed algorithm promises a good possibility for forecasting the yield at harvesting on the basis of June and July samples.

Key words: image analysis, apple, Malus domestica, yield, fruit, diameter

CITATED REFERENCES :

1. Grand D’Esnon A, Rabatel G, Pellenc R. Magali: a self-propelled robot to pick apples, ASAE paper No. 87-1037, 1987.

2. Jimenez AR, Jain AK, Ceres R, Pons JL. Automatic fruit recognition: a surveyand new results using Range/Attenuation images, Pattern Recognition 1999;32:1719- 36.

3. Juste F, Sevilla F. Citrus: A European project to study the robotic harvesting of Oranges, in: Proc. 3rd Int.Symp. Fruit, Nut and Vegetable Harvesting Mechanization, Denmark, Sweden, Norway, 1991;331-38.

4. Kataoka T, Bulanon DM, Hiroma T, Ota Y. Performance of Robotic Hand for Apple Harvesting, ASAE Paper No.993003, 1999.

5. Kondo NK, Hisaeda A, Monta M. Development of Strawberry Harvesting. Robotic Hand, ASAE Paper No.983117, 1998.

6. Lambrechts G. Apple EU 2001 Forecast 2001, Prognosfruit 2001, http://cmlag.fgov.be/dg2/fr/Communications/prognos-algemeen.pdf., 2001.

7. Perez AJ, Lopez F, Benlloch JV, Christensen S. Colour and shape analysis techniques for weed detection in cereal feels. Comput. Electron. Agric. 2000; 25:197-212.

8. Peterson DL, Anger WC, Bennedsen BS, Wolford SD.. A system approach to robotic bulk harvesting of apples. ASAE Paper No. 99-1075, 1999.

9. Ramos K, Lieberz SM. Prognosfruit 2003 – European Crop Forecast Convention, 7 s., http://www.fas.usda.gov/gainfiles/200308/14598 5760.doc. 2003.

10. Stajnko D, Lakota M. Using image processing and analysis techniques for counting apple fruits in the orchard, Horticultural Science (Prague) 2001; 28(3): 95-99.

11. Stajnko D, Lakota M, Ho~evar M. Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Comput. Electron. Agric. 2004;42:31-42.

12. Steward BL, Tian LF. Real-Time Machine Vision Weed-Sensing. ASAE Paper No. 98-3033, 1998.

13. Tian L, Slaughter DC, Norris RF. Outdoor field machine vision identification of tomato seedlings for automated weed control. Transactions of ASAE 1997;40:(6):1761-68.

14. Welte HF. Forecasting harvest fruit size during the growing season. Acta Horticulturae 1990;276:275-82.

15. Winter, F. Modelling the biological and economic development of an apple orchard. Acta Horticulturae 1986;160:353-60.