Author(s): Tedesco-Oliveira, Danilo [UNESP] ; Pereira da Silva, Rouverson [UNESP] ; Maldonado, Walter [UNESP] ; Zerbato, Cristiano [UNESP]
Date: 2020
Persistent ID: http://hdl.handle.net/11449/201593
Origin: Oasisbr
Subject(s): Deep learning; Object detection; Smart harvesting; Yield prediction; Deep learning; Deep learning; Object detection; Object detection; Smart harvesting; Smart harvesting; Yield prediction; Yield prediction
Made available in DSpace on 2020-12-12T02:36:38Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-04-01
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
One way to improve the quality of mechanized cotton harvesting is to change harvester settings and adjustments throughout the process, according to information obtained during the operation. We believe that yield predictions are important for managing the quality of operation, aiming at increasing efficiency and reducing losses. Therefore, this study aimed to develop an automated system for cotton yield prediction from color images acquired by a simple mobile device. We propose a robust approach to environmental conditions, training detection algorithms with images acquired at different times throughout the day, and evaluating three different scenarios (low-, average-, and high-demand computational resources). The experimental results for the average demand computational scenario, which are suitable for real-time deployment on low-cost devices such as smartphones and other ARM-processed devices, indicated the possibility of counting bolls using images acquired at different times throughout the day, with mean errors of 8.84% (∼5 bolls). Furthermore, we observed a 17.86% error when predicting yield using 205 images from the testing dataset, which is equivalent to about 19.14 g.
São Paulo State University School of Agricultural and Veterinary Sciences (UNESP/FCAV)
São Paulo State University School of Agricultural and Veterinary Sciences (UNESP/FCAV)
