Development and evaluation of a targeted orchard sprayer using machine vision technology

ماشین های کشاورزی

Article 8, Volume 6, Issue 2 - Serial Number 12, 2016, Pages 362-375   

Document Type: Research Article

DOI: 10.22067/jam.v6i2.37220

Authors

H. Asaei; A. A. Jafari^* ; M. Loghavi

Department of Biosystems Engineering, Shiraz University, Iran

Abstract

Introduction
In conventional methods of spraying in orchards, the amount of pesticide sprayed, is not targeted. The pesticide consumption data indicates that the application rate of pesticide in greenhouses and orchards is more than required. Less than 30% of pesticide sprayed actually reaches nursery canopies while the rest are lost and wasted. Nowadays, variable rate spray applicators using intelligent control systems can greatly reduce pesticide use and off-target contamination of environment in nurseries and orchards. In this research a prototype orchard sprayer based on machine vision technology was developed and evaluated. This sprayer performs real-time spraying based on the tree canopy structure and its greenness extent which improves the efficiency of spraying operation in orchards.
Materials and Methods
The equipment used in this study comprised of three main parts generally: 1- Mechanical Equipment 2- Data collection and image processing system 3- Electronic control system
Two booms were designed to support the spray nozzles and to provide flexibility in directing the spray nozzles to the target. The boom comprised two parts, the vertical part and inclined part. The vertical part of the boom was used to spray one side of the trees during forward movement of the tractor and inclined part of the boom was designed to spray the upper half of the tree canopy.
Three nozzles were considered on each boom. On the vertical part of the boom, two nozzles were placed, whereas one other nozzle was mounted on the inclined part of the boom. To achieve different tree heights, the vertical part of the boom was able to slide up and down. Labview (version 2011) was used for real time image processing. Images were captured through RGB cameras mounted on a horizontal bar attached on top of the tractor to take images separately for each side of the sprayer. Images were captured from the top of the canopies looking downward.
The triggering signal for actuating the solenoid valves was initially sent to the electronic control unit as the result of image processing. Electronic control unit was used to adjust the right time of spraying based on the signals received from the encoder to precisely spray the targeted tree. The distance between the camera and spraying nozzles was considered in the microcontroller program. The solenoid would be turned off and stop the spraying when the vision system realized that there was a gap between the trees.
Water sensitive papers (WSP) were used to evaluate the sprayer performance in prompt spraying of the trees and cutting off at hollow spaces between the trees.
Water sensitive papers were attached to three ropes extended along the movement direction of the tractor at top, middle, and bottom of the trees so that each tree comprised 9 WSPs whereas other 9 WSPs were placed at each gap between two successive trees. Three levels of forward speed of 2 km h-1, 3.5 km h-1and 5 km h-1 was tried in these experiments to evaluate the effect of forward speed on spraying performance. Experiments were conducted in three replications. Liquid consumption of the sprayer designed in this research was compared with the conventional overall spraying.
Results and Discussion
Analysis of variances of data gained from water sensitive paper corresponding to the sprayed areas showed a significant effect of forward speed on prompt spraying.
Comparison of means of spraying coverage on WSPs at different forward speeds with four replications showed that the maximum amount of targeted sprayed pesticide has been achieved at the lowest speed (2 km h-1) while the lowest amount of sprayed was seen at the maximum speed. Although higher forward speed is preferred because it increases the operation capacity of the sprayer, increasing the forward speed of the sprayer reduces the coverage density of the liquids on WSPs because the output rates of the nozzles are constant. Therefore, in cases that higher forward speed is demanded, more nozzles should be added to the sprayer booms
Comparison between the liquid consumptions of the proposed system and conventional overall spraying showed that in this study, up to 54% less material has been used for the experiment in olive orchard.
Conclusions
The sprayer designed in this study was able to detect the gap between the trees in orchards using a machine vision system to stop the spraying on places where no tree exists. Results showed that employing the new sprayer decreased a significant amount of spray liquids which can be important both economically and environmentally. Considering to lack of pesticide spraying in the hollow spaces between the trees, certainly, more significant reduction is expected to achieve in young orchards where trees are small and there are larger gaps between the trees

Keywords

Chemical application; Image processing; Intelligent control; Orchard sprayer; Pollution environment

References

1. Chen, Y., H. Zhu, H. Ozkan, R. Derksen, and C. Krause. 2013. Spray drift and off-target loss reduction with a precision air-assisted sprayer. Transactions of the ASABE 56: 1273-1281.

2. Chen, Y., H. Ozkan, H. Zhu, R. Derksen, and C. Krause. 2013. Spray deposition inside tree canopies from a newly developed variable-rate air assisted sprayer. Transactions 56: 1263-1272.

3. Deveau, J. 2009. Six elements of effective spraying in orchards and vineyards. Ontario, Ministry of Agriculture, Food and Rural Affairs.

4. Giles, D., M. Delwiche, and R. Dodd. 1988. Electronic measurement of tree canopy volume. Transactions of the ASAE 31: 264-272.

5. Giles, D., M. Delwiche, and R. Dodd. 1989. Sprayer control by sensing orchard crop characteristics: Orchard architecture and spray liquid savings. Journal of Agricultural Engineering Research 43: 271-289.

6. Hoçevar, M., B. Sirok, V. Jejcic, T. Godesa, M. Lesnik, and D. Stajnko. 2010. Design and testing of an automated system for targeted spraying in orchards. Journal of Plant Diseases and Protection (JPDP) 117: 71-79.

7. Lee, K., and R. Ehsani. 2008. A laser-scanning system for quantification of tree-geometric characteristics. ASABE Paper No. 083980, ASABE, St. Joseph, MI.

8. Loni, R., and M. Loghavi. 2014. Development and evaluation of a gas flamer with the ability of targeted-discrete flaming in locating and eradicating weeds. Journal of Agricultural Machinery 4: 88-97. (In Farsi).

9. Ohta, Y. I., T. Kanade, and T. Sakai. 1980. Color information for region segmentation. Computer graphics and image processing 13: 222-241.

10. Pierce, F. J., and P. Nowak. 1999. Aspects of precision agriculture. Advances in Agronomy 67: 1-85.

11. Plant, R. E. 2001. Site-specific management: the application of information technology to crop production. Computers and Electronics in Agriculture 30: 9-29.

12. Rosell Polo, J. R., R. Sanz, J. Llorens, J. Arno, A. Escolà, M. Ribes-Dasi, J. Masip, F. Camp, F. Gràcia, and F. Solanelles. 2009. A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosystems engineering 102: 128-134.

13. Solanelles, F., A. Escolà, S. Planas, J. Rosell, F. Camp, and F. Gràcia. 2006. An electronic control system for pesticide application proportional to the canopy width of tree crops. Biosystems Engineering 95: 473-481.

14. Subramanian, V., T. F. Burks, and A. Arroyo. 2006. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Computers and electronics in agriculture 53: 130-143.

15. Wei, J., and M. Salyani. 2004. Development of a laser scanner for measuring tree canopy characteristics: Phase 1. prototype development. Transactions of the ASABE 47: 2101-2107.

16. Wei, J., and M. Salyani. 2005. Development of a laser scanner for measuring tree canopy characteristics: Phase 2. Foliage density measurement. Transactions of the ASABE 48: 1595-1601.

17. Zhu, H., R. Derksen, H. Guler, C. Krause, and H. Ozkan. 2006. Foliar deposition and off-target loss with different spray techniques in nursery applications. Transactions of the ASABE 49: 325-334.