Design of a bistatic LIDAR system and simulating the performance for early detection of rice leaf blast from unmanned aircraft

Unmanned Aircraft Systems (UAS), also referred to as drones, are capable of autonomously accomplishing an increasing number of duties with basically greater efficiency compared to human involvement. The purposes of UAS researchers are to reinforce the safety levels, mission efficacy and autonomy, in addition to investigate supplementary capacities. Crop health monitoring is an encouraging application of UAS, since the early detection of plant diseases can increase classical crop monitoring methods (i.e. vision inspection) to assist timely alleviation, offering considerable benefits of crop yield and quality, correspondingly. This article introduces a proposed bistatic Light Detection and Ranging (LIDAR) associated with UAS-based Electro-Optics/Infrared (EO/IR) system for early detection of crop diseases. The anomalies in carbon dioxide (CO2) concentration, which is above the crop canopy, potentially according to disruptions in the plant photosynthesis, are measured over extended observation periods by the proposed bistatic LIDAR system. In particular, this paper discusses the application of the bistatic LIDAR to the early detection of rice leaf blast (RLB) in rice paddy and presents the initial modelling and simulation activities performed to predict the sensor performance. The experimental activities will be conducted as the next stages based on the results of this analysis.

Automated summary

Unmanned Aircraft Systems (UAS) can enhance the efficiency of crop health monitoring through autonomous capabilities. This article introduces a proposed method for early detection of crop diseases using a bistatic LIDAR system and UAS-based Electro-Optics/Infrared (EO/IR) system, measuring anomalies in CO2 concentration above the crop canopy.