Estimativa da germinação de sementes de Pityrocarpa moniliformis (Benth.) Luckow & R. W. Jobson usando Deep Learning
The possibility of estimating the germination of a seed lot through the use of Deep Learning has shown potential as a complementary method to the analysis of seed quality. In this research, we evaluated the efficiency of using Deep Learning, with convolutional neural networks, to estimate the ger...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/47623 |
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| Resumo: | The possibility of estimating the germination of a seed lot through the use of Deep Learning
has shown potential as a complementary method to the analysis of seed quality. In this
research, we evaluated the efficiency of using Deep Learning, with convolutional neural
networks, to estimate the germination of Pityrocarpa moniliformis (Benth.) Luckow & R. W.
Jobson seeds. 1000 seeds were randomly selected from four lots, 250 seeds from each,
which were used in germination tests and computational analyses. A scanner was used to
capture the images of the seeds, from which the images of each seed were obtained
individually. The seed images were used to implement, train and test the convolutional neural
networks in the computational algorithm created in this research, aiming at comparing the
results obtained from the computational analysis with those of the individual germination of
each seed. Therefore, after acquiring the images, the seeds were placed to germinate,
identifying each one individually. After the germination period, the seeds were divided into
two classes: germinated (0) and non-germinated (1). From the images of the seeds before
germination, and with the individual result of each seed, the computational analysis was
carried out. The pre-trained networks after five epochs of execution indicated a tendency to
improve accuracy, however, there were also signs of overfitting, since the performance in the
training data was better than the validation (test) data. The recall (sensitivity) was greater
than 90% in all models for the class of germinated seeds. The recall value was much lower
for the non-germinated class, both below 20%. For the model proposed, 85% of recall was
obtained for the class of non-germinated seeds and 18% for the germinated ones, which may
have occurred due to the overlap of the classes of interests. The results of the pre-trained
networks and the proposed model proved to be inefficient, as they cannot adequately
distinguish the classes of interest to assess the efficiency of using Deep Learning to estimate
the germination of Pityrocarpa moniliformis seeds, but the analyzes indicate the need to
improve and adjust the pre-processing of the images and require more investigation time and
more tests to configure the models. |
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