Machine learning models for detecting and isolating weeds from strawberry plants using UAVs

Noah T. Renella; Subodh Bhandari; Amar Raheja

doi:10.1117/12.2664408

13 June 2023 Machine learning models for detecting and isolating weeds from strawberry plants using UAVs

Noah T. Renella, Subodh Bhandari, Amar Raheja

Proceedings Volume 12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII; 1253904 (2023) https://doi.org/10.1117/12.2664408
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Noah T. Renella, Subodh Bhandari, and Amar Raheja "Machine learning models for detecting and isolating weeds from strawberry plants using UAVs", Proc. SPIE 12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII, 1253904 (13 June 2023); https://doi.org/10.1117/12.2664408

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