Presa autonoma attraverso una pinza sensoriale basata su ventosa
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Autore
Adhami, Romina <1997>
Data
2023-12-19Disponibile dal
2023-12-21Abstract
In the realm of agricultural quality control, this study pioneers a non-visual machine learning approach, uniting a sensorized, octopus-inspired suction cup with machine learning algorithms, to improve the assessment of Hayward Kiwifruit ripeness. Four key machine learning algorithms—Decision Trees, Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN)—are strategically employed to process tactile data and gauge kiwifruit ripeness. Through a meticulous nested cross-validation procedure, Support Vector Machines (SVM) emerge as the top-performing model, boasting a mean accuracy of 98.11%.
This research represents an advancement in accurately and efficiently evaluating kiwifruit ripeness. Through the collaborative integration of a sensorized suction cup and machine learning model, this work introduces valuable insights that can contribute to the agricultural field. The proposed non-visual, non-destructive technology not only addresses the unique challenges of kiwifruit ripeness detection but also presents a scalable solution with broader applications across the agricultural sector.
Moreover, this study extends its impact by exploring the real-time application of the developed model. Seamlessly integrating the sensorized suction cup and machine learning algorithms into real-world scenarios, the research demonstrates the practicality and efficacy of the proposed approach in dynamic agricultural environments. The real-time application enhances the versatility of the technology, further solidifying its potential to improve fruit quality assessment practices on a larger scale. This research, therefore, represents a contribution to both the scientific understanding of ripeness detection and the practical implementation of cutting-edge technology in agriculture.
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Nel campo del controllo qualità agricola, questo studio apre la strada a un approccio di machine learning non visivo, unendo una ventosa sensorizzata In the realm of agricultural quality control, this study pioneers a non-visual machine learning approach, uniting a sensorized, octopus-inspired suction cup with machine learning algorithms, to improve the assessment of Hayward Kiwifruit ripeness. Four key machine learning algorithms—Decision Trees, Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN)—are strategically employed to process tactile data and gauge kiwifruit ripeness. Through a meticulous nested cross-validation procedure, Support Vector Machines (SVM) emerge as the top-performing model, boasting a mean accuracy of 98.11%.
This research represents an advancement in accurately and efficiently evaluating kiwifruit ripeness. Through the collaborative integration of a sensorized suction cup and machine learning model, this work introduces valuable insights that can contribute to the agricultural field. The proposed non-visual, non-destructive technology not only addresses the unique challenges of kiwifruit ripeness detection but also presents a scalable solution with broader applications across the agricultural sector.
Moreover, this study extends its impact by exploring the real-time application of the developed model. Seamlessly integrating the sensorized suction cup and machine learning algorithms into real-world scenarios, the research demonstrates the practicality and efficacy of the proposed approach in dynamic agricultural environments. The real-time application enhances the versatility of the technology, further solidifying its potential to improve fruit quality assessment practices on a larger scale. This research, therefore, represents a contribution to both the scientific understanding of ripeness detection and the practical implementation of cutting-edge technology in agriculture.
Tipo
info:eu-repo/semantics/masterThesisCollezioni
- Laurea Magistrale [4734]