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All Journal Journal of Robotics and Control (JRC) Sisfo: Jurnal Ilmiah Sistem Informasi
Juarto, Budi
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Smart Farming Using Robots in IoT to Increase Agriculture Yields: A Systematic Literature Review Widianto, Mochammad Haldi; Juarto, Budi
Journal of Robotics and Control (JRC) Vol 4, No 3 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i3.18368

Abstract

Robots are beneficial in everyday life, especially in helping food security in the agricultural industry. Smart farming alone is not enough because smart farming is only automated without mobile hardware. The existence of robots can minimize human involvement in agriculture so that humans can maximize activities outside of farms. This Study aims to review articles regarding robots in smart farming to increase agriclture yields. This article systematically uses the systematic literature review method utilizing the Preferred reporting items for systematic review and meta-analyses (PRISMA) by submitting 3 Research Questions (RQ). According to the authors of the 3 RQs, it is necessary to represent the function and purpose of robots in farms and to be used in the context of the importance of robots in agriculture because of the potential impact of increase agriculture yields. This Research contributes to finding and answering 3 RQ, which are the roots of the use of robots. The results taken, the authors get 116 articles that can be reviewed and answered RQ and achieve goals. RQ 1 was responded to with the article's country of origin, research criteria, and the year of the article. In RQ 2 the author answered that Research often carried out 6 schemes, then the most Research was (Challenge Robots, Ethics, and Opinions in Agriculture) and (Design, Planning, and Robotic Systems in Agriculture). Finally, in RQ 3, the author describes the research scheme based on understanding related Research. The author hopes this basic scheme can be a benchmark or a new direction for future researchers and related agricultural industries to improve agricultural quality.
Mobile Deep Learning-Based Coffee Bean Quality Classification and Smartphone Integration Using Transfer Learning Juarto, Budi; Yulianto, Yulianto
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27030

Abstract

Manually classifying good quality coffee beans is subjective, can take a considerable amount of time and is hard to standardize among various operators. For this research, computer vision was used to create a classifying system, dividing coffee bean pictures into defect and Good Quality. Based on mobile execution performance, we evaluated five existing computer vision transfer learning models. Image datasets include those used to create it, public online collections of coffee bean images, a primary set collected for use during research and the full combined 1,102-image collection broken down into a training (858 pictures), validation (114 pictures) and test (130 pictures) set. We made images of the same 224x224 resolution, then used an augmentation pipeline that rotated, flipped randomly horizontally and added color changes to increase robustness. Normalized the pixel intensities using statistics gathered for ImageNet. Models were trained identically and used Adam for optimizer and a learning rate, batch size and epoch quantity. Densely Connected Convolutional Neural Network (DenseNet121), EfficientNetB0, MobileNetV2, Residual Network (ResNet50) and Xception all performed at equal settings during experimentation. The top accuracy level came from EfficientNetB0 and Xception, both reaching 96.92% on the testing data. We selected EfficientNetB0 as our core model for its performance, small size and steady use on a smartphone (as seen in the application prototype we made), but it was still a solid performing alternative to Xception. The Android prototype that came from our study supported photo input using either a camera or file upload to provide instant quality status. Transfer learning's ability to enable the use of models capable of automated and consistent assessment for coffee bean quality control would be an improvement in small coffee operations.
Co-Authors WIDIANTO, MOCHAMMAD HALDI Yulianto Yulianto