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Real-time visual inspection system for grading fruits using computer vision and deep learning techniques

Information Processing in Agriculture 2022年第1期9卷 24-37页

作者： Nazrul Ismail owais a.malik Digital Science Faculty of ScienceUniversiti Brunei DarussalamJln Tungku LinkGadong BE1410Brunei Darussalam

Traditional manual visual grading of fruits has been one of the important challenges faced by the agricultural industry due to its laborious nature as well as inconsistency in inspec-tion and classification *** defects detection using computer vision and machine learning has become a promising area of research with a high and direct impact on the domain of visual *** this study,we propose an efficient and effective machine vision system based on the state-of-the-art deep learning techniques and stack-ing ensemble methods to offer a non-destructive and cost-effective solution for automat-ing the visual inspection of fruits’freshness and *** trained,tested and compared the performance of various deep learning models including ResNet,DenseNet,MobileNetV2,NASNet and EfficientNet to find the best model for the grading of *** proposed system also provides a real time visual inspection using a low cost Raspberry Pi module with a camera and a touchscreen display for user *** real time sys-tem efficiently segments multiple instances of the fruits from an image and then grades the individual objects(fruits)*** system was trained and tested on two data sets(apples and bananas)and the average accuracy was found to be 99.2% and 98.6% using EfficientNet model for apples and bananas test sets,***,a slight improvement in the recognition rate(0.03% for apples and 0.06% for bananas)was noted while applying the stacking ensemble deep learning *** performance of the developed system has been found higher than the existing methods applied to the same data sets ***,during real-time testing on actual samples,the accuracy was found to be 96.7% for apples and 93.8% for bananas which indicates the efficacy of the developed system.

关键词： Deep learning Fruit classification Computer vision Real-time system Raspberry Pi Agriculture

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Accurate Location Estimation of Smart Dusts Using Machine Learning

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Computers, Materials & Continua 2022年第6期71卷 6165-6181页

作者： Shariq Bashir owais Ahmed malik Daphne Teck Ching Lai Institute of Applied Data Analytics(IADA) Universiti Brunei Darussalam(UBD)GadongBE1410Brunei Darussalam School of Digital Sciences(SDS) Universiti Brunei Darussalam(UBD)GadongBE1410Brunei Darussalam

Traditional wireless sensor networks(WSNs)are not suitable for rough terrains that are difficult or impossible to access by *** dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough *** tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraftwithout manually setting their *** it is clear that a number of remote sensing applications can benefit from this technology,but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny *** raises many challenges including how to estimate the location of events sensed by the smart *** solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven *** study proposes a novel machine learning based localization algorithm for estimating the location of *** approach utilizes timestamps(time of arrival)of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the *** to the unavailability of real smart dusts,we built a simulator for analysing the accuracy of the proposed approach formonitoring forest *** experiments on the simulator show reasonable accuracy of the approach.

关键词： Smart dust sensor localization remote sensing machine learning algorithms Internet of Things sensor applications

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