OPEN ACCESS | Published on : 17-Jun-2026 | Pages: 88-99 | Doi : 10.37446/edibook252025/88-99
Global agricultural output is still severely hampered by weed infestation, which results in large yield losses and growing reliance on herbicides. Utilizing natural enemies including insects, diseases, and competitive plants, biological control of weeds provides a sustainable and environmentally beneficial substitute for chemical weed management. Recent developments in this area have improved the effectiveness, accuracy, and reach of biocontrol methods. Novel bioagents have been identified and their host-specific interactions have been clarified thanks to the merging of molecular biology, genomics, and bioinformatics. Using endophytes, microbial consortia, and synthetic biocontrol organisms has demonstrated potential for improving weed suppression in a variety of agro-ecological settings. Biological control agent delivery and monitoring systems have also been enhanced by advancements in unmanned aerial vehicles (UAVs), artificial intelligence (AI), and remote sensing. Precision weed control has become more common thanks to these technological developments, which also reduce environmental hazards and increase biodiversity.
Artificial intelligence, biocontrol, environment, weeds
Ambika, S. R., & Jayachandra. (1980). Suppression of plantation crops by Eupatorium weed. Current Science, 49(22), 874–875.
Azghadi, M. R., Olsen, A., Wood, J., Saleh, A., Calvert, B., Granshaw, T., & Philippa, B. (2025). Precision robotic spot-spraying: Reducing herbicide use and enhancing environmental outcomes in sugarcane. Computers and Electronics in Agriculture, 235, 110–365.
Bhumannavar, B. S., Ramani, S., & Rajeshwari, S. K. (2007). Field release and impact of Cecidochares connexa (Macquart) (Diptera: Tephritidae) on Chromolaena odorata (L.) King and Robinson. Journal of Biological Control, 21(1), 59–64.
Buccellato, L., Byrne, M. J., & Witkowski, E. T. F. (2012). Interactions between a stem gall fly and a leaf-spot pathogen in the biological control of Ageratina adenophora. Biological Control, 61, 222–229.
Charudattan, R. (2024). Use of plant viruses as bioherbicides: The first virus-based bioherbicide and future opportunities. Pest Management Science, 80(1), 103–114.
Ellison, C. A., Day, M. D., & Witt, A. (2014). Overcoming barriers to the successful implementation of classical biological control of Mikania micrantha in the Asia-Pacific region. In F. A. C. Impson, C. A. Kleinjan, & J. H. Hoffmann (Eds.), Proceedings of the XIV International Symposium on Biological Control of Weeds (pp. 135–141). Kruger National Park, South Africa.
Gharde, Y., Singh, P. K., Dubey, R. P., & Gupta, P. K. (2018). Assessment of yield and economic losses in agriculture due to weeds in India. Crop Protection, 107, 12–18.
Guo, L., et al. (2024). Remote sensing-driven weed recognition and management using AI. Remote Sensing of Environment, 302, 113–525.
Hatcher, P. E., Moore, J., Taylor, J. E., Tinney, G. W., & Paul, N. D. (2004). Phytohormones and plant–herbivore–pathogen interactions: Integrating molecular and ecological perspectives. Ecology, 85, 59–69.
Jayanth, K. P. (1987a). Biological control of the water fern Salvinia molesta in Bangalore by Cyrtobagous salviniae. Entomophaga, 32, 163–165.
Jayanth, K. P. (1988). Biological control of water hyacinth in India by release of Neochetina bruchi. Current Science, 57(17), 968–970.
Jayanth, K. P., & Visalakshi, P. N. G. (1989). Introduction and establishment of Neochetina eichhorniae and Neochetina bruchi on water hyacinth in Loktak Lake, Manipur. Science and Culture, 55, 505–506.
Jin, Y., et al. (2023). Deep learning-based weed detection for precision biocontrol in maize and rice systems. Computers and Electronics in Agriculture, 212, 108–142.
Kapoor, V. C., & Malla, Y. K. (1979). New record of Dimeromicrus vibidia (Walker) (Hymenoptera: Torymidae), a parasite of the gall fly Procecidochares utilis. Journal of the Bombay Natural History Society, 75, 932.
Kruess, A. (2002). Indirect interaction between a fungal pathogen and a herbivorous beetle of Cirsium arvense. Oecologia, 130, 563–569.
Kumar, S. (2015). History, progress and prospects of classical biological control in India. Indian Journal of Weed Science, 47(3), 306–320.
Kumari, S., et al. (2024). Assessing ecological impacts of biological weed control using long-term satellite data. Ecological Indicators, 160, 111–572.
Mwangi, P., et al. (2023). Monitoring bioherbicide efficacy on Striga using UAV-based NDVI mapping. Precision Agriculture, 24, 138–156.
Priyanka, D., Karmal, S., Meena, S., & Sushil, K. (2024). Performance of Bt cotton evaluated in relation to mulching and weed control measures. Indian Journal of Cotton Research, 7(1), 2–8.
Roberts, J., Florentine, S., Fernando, W. D., & Tennakoon, K. U. (2022). Achievements and future challenges in bioherbicides: A global review. Plants, 11(17), 22–42.
Roslim, M. H. M., et al. (2021). Remote sensing and UAV systems for weed management: A review. Agronomy, 11(9), 1809.
Shalini, J. P., et al. (2024). Weed management under conservation agriculture. International Journal of Research in Agronomy, 8(2), 226–228.
Singh, A., et al. (2023). AI–remote sensing integration for Parthenium hysterophorus monitoring in India. Environmental Monitoring and Assessment, 195, 432.
Singh, S. P. (2004). Some success stories in classical biological control in India. Asian and Pacific Coconut Community (APCC).
Sushil Kumar. (1993). Biological control of forest and wasteland weeds in India. Annals of Entomology, 11(2), 131–153.
Sushil Kumar. (2005). Biological control of Parthenium through Zygogramma bicolorata. National Research Centre for Weed Science, Jabalpur.
Sushil Kumar. (2009). Biological control of Parthenium in India: Status and prospects. Indian Journal of Weed Science, 41(1–2), 1–18.
Sushil Kumar. (2011). Aquatic weed problems and management in India. Indian Journal of Weed Science, 43(3–4), 118–138.
