Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

Abstract

Detecting the maturity level of cloves is the initial stage in getting quality cloves. Early recognition of the maturity level of cloves is an essential stage in the clove industry. The maturity level of clove flowers can provide valuable information to clove farmers regarding clove harvest time. During the process of determining the level of maturity, it still relies on visual observation. This causes novice farmers and clove workers to still make mistakes in determining the start of the clove harvest. For this reason, in this research, initial detection of the maturity level of cloves was carried out based on images of clove flowers. There are four maturity levels: mature cloves, semimature cloves, overmature cloves, and dry cloves. The proposed research method is a modification of the transfer learning architecture. The research results show that modifying the Transfer learning architecture by adding three layers can increase system accuracy in the VGG16 and ResNet50 models by more than 5%, so that the highest accuracy obtained from modifying the VGG16 model is 95.5% and modifying the ResNet50 model is 87.75%. Meanwhile, for the VGG19 model, accuracy increased only when initializing the number of epochs to 10.