Presa autonoma attraverso una pinza sensoriale basata su ventosa

Abstract

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. convert to italian in good way ChatGPT 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.