Weeds Detection and Control in Rice Crop Using UAVs and Artificial Intelligence: A Review

Abstract

Weeds are serious issues in rice farming and undesired plants that compete for water, light, space, and nutrients, reducing crop yields. Weeds competition in rice crops can result in yield failure of up to 100% if weeds are not controlled. Furthermore, weeds will raise protection costs by harbouring other pests such as diseases, insects, and nematodes that use weeds as alternate hosts. Rice fields are infested with grassy weeds, broadleaves, and sedges, among other weeds. Weeds detection is important to identify the types of weeds in rice areas and make the precise decision to determine the method of weeds control and reduce herbicide use. Chemical, biological, and mechanical weed control strategies such as manual weeding, mechanical weeding, and herbicide use are all part of the Integrated Weed Management (IWM) approach. In the rice field, however, pesticide spraying to eliminate the weed is a very common control approach. Still, this method has become ineffective due to frequently spraying with the same type of herbicide. Early detection is required to define the type of weeds in rice fields, make a precise decision on weed management methods, and prescribe the appropriate herbicide to the rice farmer. Artificial intelligence and unmanned aerial vehicles (UAVs) were largely applied to identify the weeds in rice fields and herbicide spraying to control the weeds. UAVs, such as drones, have recently shown to have a lot of potential in agriculture, such as crop health monitoring systems, assisting in planning irrigation schedules, estimating production data, and capturing weather analysis data and weed infestation. In Malaysia, UAVs are mostly utilized for nutrition and pesticide applications, particularly by smallholder farmers and industries. The integration use of artificial intelligence including unmanned aerial vehicles drones, and various sensors, hyperspectral, multispectral, and RGB (red-green-blue) cameras, thermal and odor sensor for weed early detection methods could ensure the possibility of a better outcome in managing weed problems. This paper reviews the detection and controlling of weeds using UAVs and artificial intelligence technologies in rice crop.