Jose A. Brenes¹, Javier Ferrandez-Pastor², Jose M. Camara-Zapata³, and Gabriela Marin-Raventós¹, ¹University of Costa Rica, Costa Rica, ²University of Alicante, Spain, ³University Miguel Hernandez, Spain

In the context of smart agriculture, developing deep learning models demands large and high-quality datasets for training. However, the current lack of such datasets for specific crops poses a significant challenge to the progress of this field. This research proposes an automated method to facilitate the creation of training datasets through automated image capture and pre-processing. The method’s efficacy is demonstrated through two study cases conducted in a Cannabis Sativa cultivation setting. By leveraging automated processes, the proposed approach enables to create large-volume and high-quality datasets, significantly reducing human effort. The results indicate that the proposed method not only simplifies dataset creation but also allows researchers to concentrate on other critical tasks, such as refining image labeling and advancing artificial intelligence model creation. This work contributes towards efficient and accurate deep learning applications in smart agriculture.

Dataset Creation, Image Capturing and Pre-processing, Homography, Hough Transform, Smart Farming