DETERMINING THE OPTIMUM MATURITY OF MAIZE USING GOOGLENET (FUDMA JOURNAL OF SCIENCES, 2021)
Climatic changes, animal and human activities that lead to desertification and deforestation have affected the increase in agricultural produce especially in sub-Sahara Africa. Several efforts have been put in place to reduce these effects. However, that has not fully resolved the problem food shortages due to the growing population in sub-Sahara Africa. The application of image processing and convolutional neural network in the determination of the optimum maturity of SAMMAZ 17 variety of maize plant is being considered to mitigate for the shortage of food production. The optimum maturity is determined by using GoogleNet pre trained network on 3000 samples of maize comb captured using a camera at different maturity stages in a farmland. GoogleNet pre-trained network gave an accuracy of 82.44%. The result obtained showed a 10.44% improvement over an earlier result using Alexnet pre-trained network. The results suggest that when made operational there is a window of opportunity for increase in the production of food in sub-Sahara Africa
Keywords: Convolutional Neural Networks, Maize, Optimum Maturity