We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the vegetables (tomatoes) that had physiological diseases. Initially tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected deformed tomatoes by applying the variation of vegetable’s diameter. Later, we used a Genetic Algorithm (GA) based artificial neural network (ANN). Experiments show that the above methods can accurately identify vegetables’ shapes and meet requests of classification; the accuracy rate for the identification for vegetables with physiological diseases was up to 100%. [Nature and Science. 2005; 3(2):52-58].
Prasad, Bibhu; Mohanty, Ashima Sindhu; and Parida, Ami Kumar
"IMPLEMENTATION OF GENETIC ALGORITHM BASED ARTIFICIAL NEURAL NETWORK TO IDENTIFY VEGETABLES WITH PHYSIOLOGICAL DISEASES,"
International Journal of Smart Sensor and Adhoc Network: Vol. 3
, Article 16.
Available at: https://www.interscience.in/ijssan/vol3/iss1/16