Introduction
Various methods have been performed to control weeds in the world and the use of herbicides is one of them, but public concerns about human health have changed interest in alternative methods. Thermal methods based on flame-weeder, hot air, steam, and hot water have the potential to control weeds, but due to the high cost are not economical. Electromagnetic waves transfer energy into weeds and finally destroy them. The effect of radiation on plant mutation, high consumption of energy, and human health are problems for this approach. Unlike other methods, electrical energy is an ideal and non-chemical method for weeds. This method applies high voltage to weeds, their roots, and soil so that electric currents pass through them, and the vaporization of the liquid content of weeds kills the weeds. To increase the severity of damage to weeds, the development of a feedback mechanism is required. The ultrasonic sensor measuring physical parameters like plant height is a simple method. Some complex sensing systems include optical sensors such as infrared, and machine vision that require high-speed processors and expensive equipment. In this project, as a simple method, the monitoring of the electrical current passing through weeds was used for developing the feedback mechanism and increasing electric damage to weeds.
Materials and Methods
In this study, the system consisted of a high-voltage device that generated a 15 kV AC voltage to kill weeds, as well as a feedback mechanism that included a sensor to measure the electric current on the input of the weed killer and identify the presence of weeds and their annihilation. All parts were installed on a robotic platform, and an application on a laptop was connected to it via an access point for navigation and data reception. The system was tested in a greenhouse lab with various weeds. Initially, a test was performed to investigate the effect of high voltage on the weeds and establish relationships between the electric currents passing through weeds and their presence (before and after annihilation). During the test, the system was guided along a path and applied high voltage to kill the weeds. The feedback mechanism was then calibrated based on the extracted data on electric current relations. This allowed the system to detect weeds and their annihilation, enabling it to move to the next target once a weed had been eliminated. After calibration, a comparative test was conducted to evaluate the weed-killing efficiency of the two methods (with and without the feedback mechanism), and the results were analyzed using a t-test with p ≤ 0.01.
Results and Discussion
The observations indicated that the input electric current on the weed killer was dependent on the electric current passing through weeds. When the high-voltage electrode touched a weed, the electric current passed through it increased, and simultaneously, the high electrical energy destroyed the weed. After the removal of the weed, the electric current rapidly decreased. The average energy consumption per weed plant was estimated to be 250 joules, which can be compared with other methods. The final test comparing the use and non-use of the feedback mechanism revealed significant differences (P < 0.01) between the results obtained with and without the mechanism, demonstrating that the feedback mechanism increased the efficiency of weed annihilation. The sensing system used in the developed feedback mechanism is a simple method that is affected by the electrical resistivity of weeds. As such, it did not mistakenly detect other objects as weeds, unlike an ultrasonic mechanism. Based on these results, monitoring the electrical current passing through weeds proved to be a suitable method for developing a feedback mechanism for the weed killer to identify the presence of weeds and their annihilation.
Conclusion
The use of high voltage as a non-chemical and alternative method for weed control has shown promising results. The study revealed that measuring the electric current applied to the weed killer was an effective and straightforward approach to developing a feedback mechanism. This mechanism aids in identifying the presence of weeds and ensuring their elimination by intensifying the damage inflicted on them through the application of high electrical energy. To further enhance the efficiency and speed of weed control, future research should consider integrating an automatic guidance mechanism with the weed killer. |
- Astatkie, T., Rifai, M., Havard, P., Adsett, J., Lacko-Bartosova, M., & Otepka, P. (2007). Effectiveness of hot water, infrared and open flame thermal units for controlling weeds. Biological Agriculture & Horticulture, 25, 1-12. https://doi.org/10.1080/01448765.2007.10823205
- Birrell, S., Hughes, J., Cai, J. Y., & Iida, F. (2020). A field‐tested robotic harvesting system for iceberg lettuce. Journal of Field Robotics, 37, 225-245. https://doi.org/10.1002/rob.21888
- Blasco, J., Aleixos, N., Roger, J., Rabatel, G., & Moltó, E. (2002). AE-Automation and emerging technologies: Robotic weed control using machine vision. Biosystems Engineering, 83, 149-157. https://doi.org/10.1006/bioe.2002.0109
- Bond, W., & Grundy, A. (2001). Non‐chemical weed management in organic farming systems. Weed Research, 41, 383-405. https://doi.org/10.1046/j.1365-3180.2001.00246.x
- Brighenti, A. M., & Brighenti, D. M. (2009). Weed control in organic soybean using electrical discharge. Ciência Rural, 39(8), 315-2319. https://doi.org/10.1590/S0103-84782009000800007
- Derrick, B. (2017). How to compare the means of two samples that include paired observations and independent observations: A companion to Derrick, Russ, Toher and White (2017). The Quantitative Methods for Psychology, 13, 120-126. https://doi.org/10.20982/tqmp.13.2.p120
- Diprose, M., Benson, F., & Willis, A. (1984). The effect of externally applied electrostatic fields, microwave radiation and electric currents on plants and other organisms, with special reference to weed control. The Botanical Review, 50, 171-223. https://doi.org/10.1007/BF02861092
- Dworak, V., Selbeck, J., Dammer, K. H., Hoffmann, M., Zarezadeh, A. A., & Bobda, C. (2013). Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems. Sensors, 13, 1523-1538. https://doi.org/10.3390/s130201523
- Ehlert, D., Hammen, V., & Adamek, R. (2003). On-line sensor pendulum-meter for determination of plant mass. Precision Agriculture, 4, 139-148. https://doi.org/10.1023/A:1024553104963
- Khalil, M., Johnson, T., & Lamar, C. (2005). Comparison of computer‐based and paper‐based imagery strategies in learning anatomy. Clinical Anatomy: The Official Journal of the American Association of Clinical Anatomists and the British Association of Clinical Anatomists, 18, 457-464. https://doi.org/10.1002/ca.20158
- Legg, M., & Bradley, S. (2020). Ultrasonic Arrays for Remote Sensing of Pasture Biomass. Remote Sensing, 12, 111. https://doi.org/10.3390/rs12010111
- Marx, C., Barcikowski, S., Hustedt, M., Haferkamp, H., & Rath, T. (2012). Design and application of a weed damage model for laser-based weed control. Biosystems Engineering, 113, 148-157. https://doi.org/10.1016/j.biosystemseng.2012.07.002
- Morin, L. (2020). Progress in Biological Control of Weeds with Plant Pathogens. Annual review of phytopathology, 58. https://doi.org/10.1146/annurev-phyto-010820-012823
- Paap, A. J. (2014). Development of an optical sensor for real-time weed detection using laser based spectroscopy. [Doctoral dissertation, Edith Cowan University]. https://ro.ecu.edu.au/theses/1282
- Peltzer, S., Hashem, A., Osten, V., Gupta, M., Diggle, A., Riethmuller, G., Douglas, A., Moore, J., & Koetz, E. (2009). Weed management in wide‐row cropping systems: a review of current practices and risks for Australian farming systems. Crop and Pasture Science, 60, 395-406. https://doi.org/10.1071/CP08130
- Rask, A. M., & Kristoffersen, P. (2007). A review of non‐chemical weed control on hard surfaces. Weed Research, 47, 370-380. https://doi.org/10.1111/j.1365-3180.2007.00579.x
- Sahin, H., & Yalınkılıc, M. (2017). Using Electric Current as a Weed Control Method. European Journal of Engineering Research and Science, 2, 59-64. https://doi.org/10.24018/ejeng.2017.2.6.379
- Slaughter, D., Giles, D., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61, 63-78. https://doi.org/10.1016/j.compag.2007.05.008
- Utstumo, T., Urdal, F., Brevik, A., Dørum, J., Netland, J., Overskeid, Ø., Berge, T. W., & Gravdahl, J. T. (2018). Robotic in-row weed control in vegetables. Computers and Electronics in Agriculture, 154, 36-45. https://doi.org/10.1016/j.compag.2018.08.043
- Vigneault, C., & Benoît, D. L. (2001). Electrical weed control: theory and applications. Pages 174-188. Physical control methods in plant protection, Springer. https://doi.org/10.1007/978-3-662-04584-8_12
- Yuan, W., Li, J., Bhatta, M., Shi, Y., Baenziger, P. S., & Ge, Y. (2018). Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS. Sensors, 18, 3731. https://doi.org/10.3390/s18113731
- Zwietering, M., Jongenburger, I., Rombouts, F., & Van't Riet, K. (1990). Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 56, 1875-1881. https://doi.org/10.1128/AEM.56.6.1875-1881.1990
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