Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations.


    Access

    Download


    Export, share and cite



    Title :

    Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone




    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Onboard visual drone detection for drone chasing and collision avoidance

    Makirin, M. K. / Wastupranata, L. M. / Daffa, A. | TIBKAT | 2021


    Onboard control unit for a drone system, drone and drone system comprising the onboard control unit

    BOS FRÉDÉRIC / BERTOLACCI THIERRY | European Patent Office | 2020

    Free access

    ONBOARD CONTROL UNIT FOR A DRONE SYSTEM, DRONE AND DRONE SYSTEM COMPRISING THE ONBOARD CONTROL UNIT

    BOS FREDERIC / BERTOLACCI THIERRY | European Patent Office | 2020

    Free access

    Efficient Drone Hijacking Detection using Onboard Motion Sensors

    Feng, Zhiwei / Guan, Nan / Lv, Mingsong et al. | British Library Conference Proceedings | 2017


    Detection of Visibility Conditions through use of Onboard Cameras

    Hautiere, N. / Labayrade, R. / Aubert, D. et al. | British Library Conference Proceedings | 2005