International Journal of Agriculture Extension and Social Development
2025, Vol. 8, Issue 5, Part D
AI in weed management
S Gangadharan, P Lakshmi Chakravarthy, N Moushid, S Krishna Teja, MV Mahalakshmi and SK Afzal
How Artificial Intelligence Impacts Agriculture AI in agriculture has significantly advanced conventional farming in several aspects and one of them includes the weed control. The present work introduces AI models that are able to detect, classify, and manage weeds in an effective manner. High-resolution crop images are processed using deep learning models like CNN, Transformer based models, etc., to discriminate between crops and weeds. The technology is also being used in drones and agricultural robots to enable the real-time detection of weeds so herbicides can be applied with precision.
This precise weed control method reduces the use of herbicides, reduces environmental pollution, and tum costs.
'Early weed detection makes it possible to avoid competition for crops' resources which helps to maximize the yield and sustainability. The paper also presents the expansion of AI-empowered weed management solutions to increase their applicability to small and large scale agriculture. Two AI-based systems based on different methods - a smartphone app and a cloud service - are described to improve their accessibility and application.
In addition to precision agriculture, weed classification based on AI contributes to maintenance of the ecosystem and promotion of the biodiversity. AI systems can selectively kill weeds, without killing beneficial crops, realizing green agriculture. Information obtained from AI models could be used to inform understanding of weed behaviour, regional mapping of weeds and long term solutions for containing alien weed problems affecting crop productivity.
Better still, this paper discusses how AI weed management is being integrated into comprehensive precision agriculture systems. When weed recognition is integrated with soil monitoring, irrigation control and crop disease recognition, AI can support the overall sustainable agriculture. However, the application of these technologies is difficult, because of the dependency on good quality training data, dealing with environmental variability and investment on initial hardware and implementation cost.
S Gangadharan, P Lakshmi Chakravarthy, N Moushid, S Krishna Teja, MV Mahalakshmi, SK Afzal. AI in weed management. Int J Agric Extension Social Dev 2025;8(5):245-249. DOI: 10.33545/26180723.2025.v8.i5d.1892
