Título del libro: Proceedings Of The International Joint Conference On Neural Networks
Título del capítulo: Vision-Based Analysis on Leaves of Tomato Crops for Classifying Nutrient Deficiency using Convolutional Neural Networks
ERNESTO MOYA ALBOR;
Autores externos:
Idioma:
Año de publicación:
2020
Palabras clave:
Agricultural robots; Convolution; Convolutional neural networks; Crops; Fruits; Plants (botany); Steel beams and girders; Agriculture applications; Economic level; Growing conditions; Monitoring and control; Nutrient deficiency; Nutrient levels; Tomato plants; Vision based monitoring systems; Nutrients
Resumen:
Tomato crops are one of the most important agricultural products at economic level in the world. However, the quality of the tomato fruits is highly dependent to the growing conditions such as the nutrients. One of consequences of the latter during tomato harvesting is nutrient deficiency. Manually, it is possible to anticipate the lack of primary nutrients (i.e. nitrogen, phosphorus and potassium) by looking the appearance of the leaves in tomato plants. Thus, this paper presents a supervised vision-based monitoring system for detecting nutrients deficiencies in tomato crops by taking images from the leaves of the plants. It uses a Convolutional Neural Network (CNN) to recognize and classify the type of nutrient that is deficient in the plants. First, we created a data set of images of leaves of tomato plants showing different symptoms due to the nutrient deficiency. Then, we trained a suitable CNN-model with our images and other augmented data. Experimental results showed that our CNN-model can achieve 86.57% of accuracy. We anticipate the implementation of our proposal for future precision agriculture applications such as automated nutrient level monitoring and control in tomato crops. © 2020 IEEE.
Entidades citadas de la UNAM:
ISBN:
9781728169262
Editorial:
Institute of Electrical and Electronics Engineers Inc.
