Convolutional neural networks in predicting cotton yield from images of commercial fields
| Autor: | Danilo Tedesco-Oliveira, Walter Maldonado, Cristiano Zerbato, Rouverson Pereira da Silva |
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| Přispěvatelé: | Universidade Estadual Paulista (Unesp) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Computer science Object detection Yield (finance) media_common.quotation_subject Real-time computing Process (computing) Forestry Deep learning 04 agricultural and veterinary sciences Horticulture Smart harvesting 01 natural sciences Convolutional neural network Computer Science Applications Yield prediction 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Quality (business) Agronomy and Crop Science Mobile device 010606 plant biology & botany media_common |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| Popis: | 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) |
| Databáze: | OpenAIRE |
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