Convolutional Neural Network Model to detect diseases in the leaves of Quinoa (Chenopodium quinoa) crop at the Centro Agronómico K'ayra, San Jeronimo, Cusco 2023

• Autores: Oscco-Ccuiro, Smit, Huashuayo-Miranda, Elias, Aquino-Cruz, Mario, Romo-Nava, María
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Nacional Micaela Bastidas de Apurímac
• Repositorio: UNMB-Riqchary
• Lenguaje: español
• OAI Identifier: oai:revistas.unamba.edu.pe:article/117
• Enlace del recurso: https://revistas.unamba.edu.pe/index.php/riqchary/article/view/117
• Nivel de acceso: acceso abierto
• Materia: Quinoa diseases; Bacterial Spot; Downy Mildew; Convolutional Neural Networks (CNN); Enfermedades de la quinua; Mancha Bacteriana; Mancha Foliar; Mildiu; Redes Neuronales Convolucionales (CNN)

In the world, crop diseases are the main cause of reduction in production quality. These diseases affect quinoa crops and a large amount of economic losses occur each year. It is essential to identify these diseases at an early stage to increase production. A visual inspection is the most common method to identify diseases, these errors are common through visual inspection. Time is a key factor in disease detection and requires experience. This study shows how image recognition can be used for disease detection. This work consisted of collecting a data set of images for leaf spot 1,120 images, for bacterial spot 850 images, for downy mildew 896 images and 1,090 healthy images for a total of 3,956 images of quinoa leaves from the K'ayra agronomic center in the Leticia sector, San Jeronimo, Cusco, Peru, of which 70% were considered for training, 20% for validation and 10% for testing. The proposed model worked correctly with an accuracy of 89.498%, which will allow quinoa farmers to detect diseases early, hopefully leading to an increase in quinoa production worldwide.