This paper presents the collaboration between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for site-specific application of chemical (herbicides, pesticides, and other chemicals). The paper shows and discusses the experimental plot used in the project, methods for collaboration between the UAVs and UGVs for detection and isolation of weeds and strawberry plants using machine learning and remote sensing techniques, and methods for site specific application of chemicals. The paper also discusses the method of communication between UAVs and UGVs for data sharing and hardware used in the project including UAVs, UGVs, sensors, and communication devices. Some experimental results are shown and discussed.
This paper presents the development and validation of machine learning models for locating strawberry plants and weeds as well as determining the health of strawberry plants. TensorFlow Lite Model Maker was used for object detection and model training using a custom dataset of marked images. The data used in the dataset are images collected from an unmanned aerial vehicle (UAV) and are annotated using LabelImg, a popular tool for annotating bounding boxes over images. The locations of the weeds and strawberry plants were found in both latitude/longitude coordinates as well as Degrees, Meters, Seconds (DMS) format by using the ground sample distance formula (GSD). The greenness indices were found by using OpenCV image alignment on the multispectral sensors to calculate the corresponding greenness index. The developed machine learning models can well predict plant health, detect weeds, and determine their locations. The overall goal of the project is to use UAV-based remote sensing and machine learning techniques for precision farming that aims to optimize the use of water and chemicals using site-specific and optimal applications water and chemicals.
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