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All Journal International Journal of Electrical and Computer Engineering InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Pengabdian Masyarakat Madani
Suradiradja, Kahfi Heryandi
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Religion Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Convolutional neural network for estimation of harvest time of forage sorghum (sorghum bicolor) cultivar samurai-1 Suradiradja, Kahfi Heryandi; Sitanggang, Imas Sukaesih; Abdullah, Luki; Hermadi, Irman
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1730-1738

Abstract

One of the economic alternatives to improve the quality of ruminant feed is combining grass as the main feed with high-protein forages such as sorghum. To get a quality sorghum harvest during the period, it must be right when it has good biomass content, nutrients, and digestibility. The problem is that measuring quality in the laboratory has additional costs and time, which is not short, causing delays. An approach with machine learning using a convolutional neural network can be a better solution. This research uses a convolutional neural network algorithm with the right architecture to estimate sorghum harvest time from imaging results of unmanned aerial vehicles. The stages of this research include data collection, pre-processing, modeling, and finally, the evaluation stage. This research compares the results of several convolutional neural network (CNN) algorithm architectural models: simple CNN, ResNet50 V2, visual geometry group-16 (VGG-16), MobileNet V2, and Inception V3. The result is determining the CNN algorithm architectural model that can estimate sorghum harvest time with maximum accuracy. The best result is the simple CNN architectural model with an accuracy of 0.95. This research shows that the classification model obtained from the CNN algorithm with a simple CNN architecture is the choice model for estimating sorghum harvest time.
Co-Authors Dani Ramdani, Dani Imas Sukaesih Sitanggang Irman Hermadi Luki Abdullah Nano, Karno Slamet Raharjo