Classification of maturity level of fuji apple fruit with fuzzy logic method

Mulyani, Evi Dewi Sri; Susanto, Jeni Poniman

doi:10.1109/citsm.2017.8089294

Cited by 9 publications

(4 citation statements)

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“…In the context of Fuji apples, the current classification methods range from 85.7% for (Mulyani & Susanto, 2017), which is based on fuzzy logic and RGB images, to 99.4% of (Pourdarbani et al, 2020) which uses color and spectral data. Since the datasets of these works are not the same, the obtained values cannot be directly compared.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…For example, Zhang et al (2020) also used Vis/NIR spectroscopy, but the dataset consisted of 846 apple samples divided into three classes: immature, harvest maturity, and eatable maturity. Mulyani and Susanto (2017) used three classes: raw, halfripe, and ripe, but the input data only contains RGB images. Although their obtained CCR is lower than the rest of methods, the advantages of using inexpensive and highly portable capture devices cannot be obviated since it can be applied with standard cameras instead of spectrometers.…”

Section: Comparison With Other Workmentioning

confidence: 99%

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Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy

Benmouna

Garcı́a-Mateos

Sabzi

et al. 2022

Food Bioprocess Technol

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The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples.

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Section: Comparison With Other Workmentioning

confidence: 99%

Section: Comparison With Other Workmentioning

confidence: 99%

Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy

Benmouna

Garcı́a-Mateos

Sabzi

et al. 2022

Food Bioprocess Technol

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show abstract

“…Desain Model Prediksi Fuzzy merupakan algoritma dengan memproses data input dan output untuk menentukan hasil akhir. Fuzzy pada dasarnya memiliki dua nilai yaitu 0 yang berarti salah dan 1 yang berarti benar [17]. Algoritma Fuzzy pada sistem menggunakan library dari scikit-fuzzy dengan bahasa pyhton 3.…”

Section: )unclassified

Prediksi Keputusan Juri Pencarian Bakat Berdasarkan Ekspresi Wajah Menggunakan Analis VGG16 dan Fuzzy

Fikriansyah,

Prasasti,

Paryasto

2022

JURNAL MULTIMEDIA NETWORKING INFORMATICS

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Ekspresi merupakan suatu alat komunikasi manusia untuk mengungkapkan rasa emosional. Saat ini teknologi pengenalan ekspresi wajah terus dikembangkan oleh penelitian untuk meningkatkan kualitas dari teknologi tersebut. Dengan hal tersebut akan dikembangkan model yang sudah ada agar dapat memprediksi keputusan manusia berdasarkan ekspresi wajahnya. Tugas Akhir ini menggunakan arsitektur VGG16 Modifikasi sebagai model klasifikasi ekspresi wajah. Datasets yang digunakan untuk klasifikasi adalah FER-2013 Modifikasi menjadi lima ekspresi yaitu angry, disgust, happy, neutral, dan surprise dengan jumlah data 23910. Model klasifikasi digunakan untuk pembacaan ekspresi wajah juri melalui video pada model prediksi. Hasil ekspresi yang terbaca akan dihitung menggunakan Logika Fuzzy dalam menentukan keputusan juri dengan hasil ‘yes’ atau ‘no’. Hasil pengujian Arsitektur VGG16 Modifikasi menggunakan kombinasi parameter terbaik diperoleh dengan epoch 100, batch size 32, learning rate 0.0001, dan split data 10% untuk validasi memperoleh akurasi latih 93% dan akurasi validasi 86%. Model dievaluasi dengan data testing sebesar 10% diluar dari data training memperoleh akurasi uji sebesar 85%. Classification Report dari evaluasi memperoleh precision 84%, recall 82%, dan f1-score 83%. Model yang dihasilkan memiliki performansi yang bagus dalam mengklasifikasikan ekspresi wajah dibandingkan dengan VGG16. Hasil prediksi keputusan juri menggunakan fuzzy memperoleh prediksi yang benar sebanyak 20:20 dari jumlah sampel pengujian.

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“…indentfirst Therefore, it is of great significance to study an automatic detection system of maturity with high recognition rate for determining the distribution area of different maturity fruits and realizing automatic cherry picking. In recent years, scholars at home and abroad have tried to use machine vision and spectral analysis methods to realize the detection of fruit maturity of apple [4], banana [5], mango [6] and persimmon [7]. Zhao Juan et al [8] established extreme learning machine and support vector regression classification models for the three maturity levels of Fuji apples, and the experimental results show that the visible/near infrared spectroscopycombined with the maturity evaluation index can Version January 27, 2024 submitted to Journal Not Specified https://www.mdpi.com/journal/notspe cified Version January 27, 2024 submitted to Journal Not Specified 2 of 13 realize the maturity classification of apples.…”

Section: Introductionmentioning

confidence: 99%

Research on cherry maturity detection based on improved DS-YOLOV8

You,

Zhang,

Wang

et al. 2024

Preprint

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In order to automatically detect the maturity of cherry fruits in the natural environment 1 and realize the automatic picking of cherry fruits, three levels of cherry fruit maturity (Green ripening 2 stage, medium ripening stage, full ripening stage) were formulated according to the changes of 3 cherry fruit phenotypic characteristics in the mature stage and the company standard GH/T 1193-2021. Aiming at the problem that the difference of adjacent cherries’ maturity characteristics is not obvious and the mutual occlusion between fruits, an improved YOLO v8 model is proposed for cherry fruit maturity detection. In this method, the Dymatic Snake Convolution (DSconv) module 7 was introduced into the YOLO v8 model as the backbone feature extraction network to reduce the number of parameters of the network. At the same time, the spatial attention mechanism (including 9 CBMA and FocalModulation modules) was added to the feature fusion network to improve the feature expression ability of the network. The experimental results show that the average precision, 11 recall and average precision of the improved YOLO v8 model under the test set are 98.6%, 98.1% and 12 98.2% respectively. Compared with Faster R-CNN, YOLO v3 and YOLO v5s, the improved YOLO v8 13 model has improved by 18.7, 0.2, 0.3 and 0.1 percentage points respectively. The results show that the 14 improved YOLO v8 model can provide technical support for the automatic picking of cherries.

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Classification of maturity level of fuji apple fruit with fuzzy logic method

Cited by 9 publications

References 0 publications

Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy

Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy

Prediksi Keputusan Juri Pencarian Bakat Berdasarkan Ekspresi Wajah Menggunakan Analis VGG16 dan Fuzzy

Research on cherry maturity detection based on improved DS-YOLOV8

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