Tamis: raspberry pi-based non-invasive system for sugarcane (saccharum officinarum) maturity classification via convolutional neural network (cnn) for real-time video processing/ Kirstentahle T. Cruz, Sofia Ysabel E. Ilagan, Lim, Brandon Bradley T., Zhi Wen, Allen T. Matic, Airiz A. Mendoza, and Stephen Raynne G. Modequillo.--

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Cruz, Kirstentahle T [author]

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Material type: Text

TextPublication details: Manila: Technological University of the Philippines, 2025.Description: xv, 138pages: 29cmContent type:

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BTH TK 870 C78 2025

Dissertation note: College Of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines, 2025. Summary: Sugarcane (Saccharum officinarum) is one of the Philippines’ most economically vital crops, contributing approximately PHP 76 billion annually and supporting over 700,000 workers. Accurate maturity assessment is essential for maximizing yield and sugar recovery. However, conventional methods involve invasive techniques, such as extracting juice from stalks to measure Brix values, which increase the risk of spoilage and disease. This study introduces TAMIS, a low-cost, user-friendly system using Raspberry Pi to non- invasively classify sugarcane maturity. A DSLR camera was used alongside the device’s initial capture functionality to collect 435 high-resolution images of the PHIL 2006-2289 sugarcane variety. Photos were taken monthly during the critical 9th to 12th month growth phase under direct sunlight to ensure consistent lighting all throughout the data collection phase. The dataset, annotated with Roboflow, was sorted into three maturity categories, namely: Low Brix, Medium Brix, and High Brix. A custom YOLOv5 model trained on this data achieved 75% classification accuracy, with 72% precision, 68% recall, and a 69% F1- score. Compared to traditional refractometers, TAMIS reduces costs by nearly threefold, making it accessible to small-scale farmers. The system provides a real-time, scalable maturity analysis without damaging sugarcane, improving efficiency over invasive methods. Experimental results highlight TAMIS as an innovative and practical tool for farmers, researchers, and industry professionals seeking accessible, ergonomic, and non- destructive solutions for sugarcane monitoring.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TK 870 C78 2025 (Browse shelf(Opens below))	c.1	Not for loan		BTH0006474

Bachelor's thesis

College Of Engineering.--
Bachelor of science in electronics engineering: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

Sugarcane (Saccharum officinarum) is one of the Philippines’ most economically
vital crops, contributing approximately PHP 76 billion annually and supporting over
700,000 workers. Accurate maturity assessment is essential for maximizing yield and sugar
recovery. However, conventional methods involve invasive techniques, such as extracting
juice from stalks to measure Brix values, which increase the risk of spoilage and disease.

This study introduces TAMIS, a low-cost, user-friendly system using Raspberry Pi to non-
invasively classify sugarcane maturity. A DSLR camera was used alongside the device’s

initial capture functionality to collect 435 high-resolution images of the PHIL 2006-2289
sugarcane variety. Photos were taken monthly during the critical 9th to 12th month growth
phase under direct sunlight to ensure consistent lighting all throughout the data collection
phase. The dataset, annotated with Roboflow, was sorted into three maturity categories,
namely: Low Brix, Medium Brix, and High Brix. A custom YOLOv5 model trained on this
data achieved 75% classification accuracy, with 72% precision, 68% recall, and a 69% F1-
score. Compared to traditional refractometers, TAMIS reduces costs by nearly threefold,
making it accessible to small-scale farmers. The system provides a real-time, scalable
maturity analysis without damaging sugarcane, improving efficiency over invasive
methods. Experimental results highlight TAMIS as an innovative and practical tool for

farmers, researchers, and industry professionals seeking accessible, ergonomic, and non-
destructive solutions for sugarcane monitoring.

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