Convolutional neural networks for citrus black spot classification
DOI:
https://doi.org/10.71068/4x9sw526Keywords:
Computer vision, convolutional neural networks, black spot, artificial intelligenceAbstract
An innovative computer vision model based on convolutional neural networks (CNN) is presented for the classification of black spot in citrus fruits. This study adopts a methodology that combines Research and Development with agile Scrum principles. Comparative evaluation with existing citrus classification models in different contexts demonstrates that our model shows significant differences in classification accuracy compared to models B and C.Statistical analysis, including the McNemar test, confirms the model's effectiveness, highlighting its reliability and competitiveness in the detection of diseases in citrus fruits. The results obtained not only provide an efficient model for the classification of black spot in citrus fruits but also promote advances in the application of artificial intelligence in agriculture. This approach suggests new directions for research and emphasizes the importance of computer vision in improving crop health. The implementation of this model can reduce economic losses and optimize productivity, providing significant benefits for both farmers and the agricultural industry.
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