Quality inspection of fertilizer granules using computer vision-a review

被引：0

作者：

Ndukwe, I. K. ^{[1
]}

Yunovidov, D. V. ^{[4
]}

Bahrami, M. R. ^{[2
,6
]}

Mazzara, M. ^{[3
]}

Olugbade, T. O. ^{[5
]}

机构：

[1] Innopolis Univ, Univ St 1, Innopolis 420500, Tatarstan, Russia

[2] Innopolis Univ, Cyber Phys Syst Lab, Univ St 1, Innopolis 420500, Tatarstan, Russia

[3] Innopolis Univ, Fac Comp Sci & Engn, Univ St 1, Innopolis 420500, Tatarstan, Russia

[4] LogicYield, Gvardeyskaya St 14, Kazan 420073, Tatarstan, Russia

[5] Univ Dundee, Dundee DD1 4HN, Scotland

[6] Samarkand Int Univ Technol, Labs Ctr, Samarkand 140100, Uzbekistan

来源：

COMPUTER OPTICS | 2025年 / 49卷 / 01期

关键词：

Quality control; computer vision; machine vision; machine learning; grains; fertilizer granules; ARTIFICIAL NEURAL-NETWORKS; MACHINE VISION; IMAGE-ANALYSIS; SOYBEAN SEEDS; RICE; CLASSIFICATION; IDENTIFICATION; GRAIN; FEATURES; KERNELS;

D O I：

10.18287/2412-6179-CO-1458

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

This research explores the fusion of computer vision and agricultural quality control. It investigates the efficacy of computer vision algorithms, particularly in image classification and object detection, for non-destructive assessment. These algorithms offer objective, rapid, and error-resistant analysis compared to human inspection. The study provides an extensive overview of using computer vision to evaluate grain and fertilizer granule quality, highlighting granule size's significance. It assesses prevailing object detection methods, outlining their advantages and drawbacks. The paper identifies the prevailing trend of framing quality inspection as an image classification challenge and suggests future research directions. These involve exploring object detection, image segmentation, or hybrid models to enhance fertilizer granule quality assessment.

引用

页码：84 / 94

页数：11

共 90 条

[1] Aspect Ratio Analysis Using Image Processing for Rice Grain Quality
Aggarwal, Amit K.
Mohan, Ratan
[J]. INTERNATIONAL JOURNAL OF FOOD ENGINEERING, 2010, 6 (05)
[2] Color classifier for symptomatic soybean seeds using image processing
Ahmad, IS
Reid, JF
Paulsen, MR
Sinclair, JB
[J]. PLANT DISEASE, 1999, 83 (04) : 320 - 327
[3] Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach
Altuntas, Yahya
Comert, Zafer
Kocamaz, Adnan Fatih
[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 163
[4] Anami BS, 2015, IJIGSP, V8, P19, DOI [10.14257/ijsip.2015.8.4.02, DOI 10.14257/IJSIP.2015.8.4.02]
[5] [Anonymous], 2014, ISO 13322-1
[6] Azman N, 2014, J Adv Agr Technol, V1, P119, DOI [10.12720/joaat.1.2.119-124, DOI 10.12720/JOAAT.1.2.119-124]
[7] MIDV-2020: a comprehensive benchmark dataset for identity document analysis
Bulatov, K. B.
Emelianova, E., V
Tropin, D., V
Skoryukina, N. S.
Chernyshova, Y. S.
Sheshkus, A., V
Usilin, S. A.
Ming, Z.
Burie, J. C.
Luqman, M. M.
Arlazarov, V. V.
[J]. COMPUTER OPTICS, 2022, 46 (02) : 252 - 270
[8] Bulatov KB, 2022, Computer Optics, V46, P436, DOI [10.18287/2412-6179-CO-1024, DOI 10.18287/2412-6179-CO-1024]
[9] SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry
Buscombe, Daniel
[J]. EARTH SURFACE PROCESSES AND LANDFORMS, 2020, 45 (03) : 638 - 651
[10] Carion Nicolas, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12346), P213, DOI 10.1007/978-3-030-58452-8_13

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