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Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based fruit classification Model

Computer Systems Science & Engineering 2023年第11期47卷 1633-1650页

作者： T.Satyanarayana Murthy Kollati Vijaya Kumar Fayadh Alenezi E.Laxmi Lydia Gi-Cheon Park Hyoung-Kyu Song Gyanendra Prasad Joshi Hyeonjoon Moon Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of International Affairs and Education Gangseo UniversitySeoul07661Korea Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from ***,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing ***,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human *** automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human ***’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several *** the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based fruit classification(AMO-SCNNFC)*** the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the *** addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature *** fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of *** performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit *** comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.

关键词： fruit classification computer vision machine learning deep learning metaheuristics

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Deep Learning-Based classification of fruit Diseases:An Application for Precision Agriculture

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Computers, Materials & Continua 2021年第2期66卷 1949-1962页

作者： Inzamam Mashood Nasir Asima Bibi Jamal Hussain Shah Muhammad Attique Khan Muhammad Sharif Khalid Iqbal Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC UniversityTaxila47040Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanSouth Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityBeirutLebanon

Agriculture is essential for the economy and plant disease must be *** recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time *** intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of ***,the convolutional neural network(CNN)techniques show a massive success for image classification *** extracts more detailed features and can work efficiently with large *** this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were ***,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based *** the fusion process,a few pieces of redundant information were added in the form of ***,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final *** the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous ***,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).

关键词： Agriculture deep learning feature selection feature fusion fruit classification

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

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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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fruits classification Using Convolutional Neural Network

Fruits Classification Using Convolutional Neural Network

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作者： Muhammad Idrees 华北电力大学(北京)

学位级别：硕士

Due to the increased global food waste that has resulted from careless production,processing,and distribution methods,food security has become an increasingly hot topic of discussion in modern *** gathered from surveys conducted by institutions around the world,such as the Food Bureau of the US,have also made it clear that an increase in food production is required to meet the needs of a rapidly expanding *** of this highlights the growing importance of optimising available resources to aid in the effective production and management of our food *** agricultural sector has realised the importance of detecting spoiled *** farmers often waste time and energy using humans to determine which fruits are fresh and which are *** don't get tired of doing the same thing over and over again like humans *** a result,the project proposes a method for reducing human labour,as well as production costs and lead times,in the agricultural sector through the detection of fruit *** flawed fruits could taint healthy ones if we don't catch them *** of its usefulness in automating routine tasks and improving the quality of the results,deep learning has found application in many different *** part of efforts to ensure that people always have access to safe and nutritious food,deep learning is being put to use both in the field and in the marketplace to boost crop yields and protect fruit *** guarantee reliable reports on the quality of fruit sold to consumers,scientists are investigating the feasibility of using deep learning for in-store inspection and grading ***,since visual evaluation is the primary basis for a purchase decision in the market,it is important to ensure the visual quality of fruits in order to drive *** automatic feature learning capabilities of CNNs make them ideal for image classification tasks like distinguishing between fresh and rotten *** pooling laye

关键词： Fresh and Rotten fruits Feature Extraction fruit Neural Network Deep Learning Convolution Neural Network(CNN) fruit classification

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Grading Method of Kiwifruit Based on Surface Defect Recognition

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Agricultural Biotechnology 2021年第4期10卷 44-47页

作者： Jingjing MA Tao YANG Hai YU Gang FANG Sichuan Vocational College of Finance and Economics Chengdu 610101China Chengdu Agricultural College Chengdu 611130China

Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was *** K-means clustering segmentation algorithm was used to segment the surface defects,and then color contrast was performed to determine whether it was a piece of defective ***,the shape features of normal fruit were extracted and an SVM classifier was designed to further determine its *** method has the advantages of low cost,simple algorithm and high efficiency,which opens a new way for fruit classification,and is of great significance to promoting the development of fruit classification industry in China and enhancing international competitiveness.

关键词： Kiwifruit Surface defect identification fruit classification

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