A Robust Convolutional Neural Network Model for Fruit Image Classification.

Autor: Kamagate, Beman Hamidja, Kopoin, N'Diffon. Charlemagne, Koffi, Dagou Dangui Augustin, Asseu, Olivier Pascal
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Zdroj: Ingénierie des Systèmes d'Information; Oct2024, Vol. 29 Issue 5, p1701-1710, 10p
Abstrakt: Modernization of agriculture is associated with increased use of energy and inputs (fertilizers, plant protection products, water), which today need to be better managed for optimization purpose and to limit risks for people and the environment. This issue has given rise to smart agriculture, a relative new approach to farming based on the integration of information and communication technologies, particularly Artificial Intelligent (AI) and Internet of Things, in which the environment (soil, relief, air, etc.) represents potential data source that should be exploited. In this work, we propose a service of smart agriculture that based on plant image recognition. A robust and simple Convolutional Neural Network (CNN) model called Kamagate, Kopoin and Dagou Neural Network (KKDNet) is developed for plant image classification. For our first experiment, we focused on tomato cultivation, which is very demanding in terms of energy and inputs. The proposed model KKDNet with less layer than other CNN architecture achieves approximately the same performance in metric like accuracy. As concerning the execution time, the other models use more execution time. we need to multiply KKDNet's execution time by a coefficient ranging from 25 to 52 to match the time required for the other architectures discussed in this study. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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