Skip to main content
SpringerLink
Log in

An effective and friendly tool for seed image analysis

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Image analysis is an essential field for several topics in the life sciences, such as biology or botany. In particular, the analysis of seeds (e.g. fossil research) can provide important information on their evolution, the history of agriculture, plant domestication, and diets knowledge in ancient times. This work presents software that performs image analysis for feature extraction and classification from images containing seeds through a novel and unique framework. In detail, we propose two plugins for ImageJ, one able to extract morphological, texture, and colour features from seed images, and another to classify seeds using the extracted features. The experimental results demonstrated the correctness and validity of both the extracted features and the classification predictions on two public seeds datasets, showing that combining the handcrafted features with the Random Forest classifier can reach outstanding performance on both datasets. The proposed tool is easily extendable to other fields of image analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

DPI:

Dots per inch

CNN:

Convolutional neural network

LO-IDB:

LOcal Image DataBase

CA-IDB:

CAnada Image DataBase

Acc:

Accuracy

Spe:

Specificity

Sen:

Sensitivity

MAvG:

Macro-average geometric

MFM:

Mean F-measure

MAvA:

Macro-average arithmetic

SVM:

Support vector machine

KNN:

K nearest neighbour

RF:

Random forest

GLCM:

Grey-level co-occurrence matrix

References

  1. Agency, CFI: https://inspection.canada.ca/active/netapp/idseed/idseed_gallerye.aspx?itemsNum=-1&famkey=&family=&keyword=&letter=A (2021). Accessed 13 August 2021

  2. Ahmad, N., Asghar, S., Gillani, S.A.: Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis. Comput. 1–20 (2021). https://link.springer.com/article/10.1007/s00371-021-02153-y#citeas

  3. Alejo, R., Antonio, J.A., Valdovinos, R.M., Pacheco-Sánchez, J.H.: Assessments metrics for multi-class imbalance learning: a preliminary study. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds.) Pattern Recognition, pp. 335–343. Springer, Berlin (2013)

    Chapter  Google Scholar 

  4. Amara, J., Bouaziz, B., Algergawy, A.: A deep learning-based approach for banana leaf diseases classification. In: Lecture Notes in Informatics (LNI), Proceedings—Series of the Gesellschaft fur Informatik (GI) (2017)

  5. Bacchetta, G., García, P.E., Grillo, O., Mascia, F., Venora, G.: Seed image analysis provides evidence of taxonomical differentiation within the Lavatera triloba aggregate (Malvaceae). Flora Morphol. Distrib. Funct. Ecol. Plants 206(5), 468–472 (2011)

    Article  Google Scholar 

  6. Bohl, E., Terraz, O., Ghazanfarpour, D.: Modeling fruits and their internal structure using parametric 3Gmap L-systems. Vis. Comput. 31(6), 819–829 (2015)

    Article  Google Scholar 

  7. Bouby, L., Figueiral, I., Bouchette, A., Rovira, N., Ivorra, S., Lacombe, T., Pastor, T., Picq, S., Marinval, P., Terral, J.F.: Bioarchaeological insights into the process of domestication of grapevine (Vitis Vinifera L.) during roman times in southern France. PLoS ONE 8(5), e63195 (2013)

    Article  Google Scholar 

  8. Campanile, G., Di Ruberto, C., Loddo, A.: An open source plugin for image analysis in biology. In: 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 162–167 (2019)

  9. Di Ruberto, C., Cinque, L.: Decomposition of two-dimensional shapes for efficient retrieval. Image Vis. Comput. 27(8), 1097–1107 (2009)

    Article  Google Scholar 

  10. Di Ruberto, C., Putzu, L.: A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 601–609 (2014)

  11. Di Ruberto, C., Fodde, G., Putzu, L.: Comparison of statistical features for medical colour image classification. In: Nalpantidis, L., Krüger, V., Eklundh, J.O., Gasteratos, A. (eds.) Computer Vision Systems, pp. 3–13. Springer, Cham (2015)

    Chapter  Google Scholar 

  12. Di Ruberto, C., Loddo, A., Putzu, L.: Detection of red and white blood cells from microscopic blood images using a region proposal approach. Comput. Biol. Med. 116, 103530 (2020)

    Article  Google Scholar 

  13. Frigau, L., Antoch, J., Bacchetta, G., Sarigu, M., Ucchesu, M., Alves, C.Z., Mola, F.: A statistical approach to the morphological classification of Prunus sp. seeds. Plant Biosyst. 154(6), 877–886 (2020)

    Article  Google Scholar 

  14. Gad, R., Abd El-Latif, A.A., Elseuofi, S., Ibrahim, H.M., Elmezain, M., Said, W.: Iot security based on iris verification using multi-algorithm feature level fusion scheme. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–6. IEEE (2019)

  15. Gajjar, R., Gajjar, N., Thakor, V.J., Patel, N.P., Ruparelia, S.: Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Vis. Comput. 1–16 (2021). https://link.springer.com/article/10.1007/s00371-021-02164-9#citeas

  16. Gonzales, R., Woods, R.: Digital Image Processing. Pearson (2018)

  17. Gulzar, Y., Hamid, Y., Soomro, A.B., Alwan, A.A., Journaux, L.: A convolution neural network-based seed classification system. Symmetry 12, 1–18 (2020)

    Article  Google Scholar 

  18. Hall, D., McCool, C., Dayoub, F., Sunderhauf, N., Upcroft, B.: Evaluation of features for leaf classification in challenging conditions. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 797–804 (2015)

  19. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  20. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973)

    Article  Google Scholar 

  21. Hu, S., Zhang, Z., Xie, H., Igarashi, T.: Data-driven modeling and animation of outdoor trees through interactive approach. Vis. Comput. 33(6), 1017–1027 (2017)

    Article  Google Scholar 

  22. ImageJ: https://imagej.net/ImageJ (2021). Accessed 7 July 2021

  23. Jing, H., He, X., Han, Q., Abd El-Latif, A.A., Niu, X.: Saliency detection based on integrated features. Neurocomputing 129, 114–121 (2014)

    Article  Google Scholar 

  24. Junos, M.H., Khairuddin, ASM., Thannirmalai, S., Dahari, M.: Automatic detection of oil palm fruits from UAV images using an improved YOLO model. Vis. Comput. 1–15 (2021)

  25. Kamilaris, A., Prenafeta-Boldú, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)

    Article  Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3–6, 2012, Lake Tahoe, Nevada, United States, pp. 1106–1114 (2012)

  27. Krogh Mortensen, A., Dyrmann, M., Karstoft, H., Nyholm Jørgensen, R., Gislum, R.: Semantic segmentation of mixed crops using deep convolutional neural network. In: CIGR-AgEng Conference (2016)

  28. Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A.: Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(5), 778–782 (2017)

    Article  Google Scholar 

  29. Landini, G.: Advanced shape analysis with imagej. In: 2th ImageJ User and Developer Conference, 7–8 November 2008, Luxembourg, pp. 6–7 (2008)

  30. Lind, R.: Open source software for image processing and analysis: picture this with imagej. In: Harland, L., Forster, M. (eds.) Open Source Software in Life Science Research. Woodhead Publishing Series in Biomedicine, pp. 131–149. Woodhead Publishing, Sawston (2012)

    Chapter  Google Scholar 

  31. Lo Bianco, M., Grillo, O., Cremonini, R., Sarigu, M., Venora, G.: Characterisation of Italian bean landraces (Phaseolus vulgaris L.) using seed image analysis and texture descriptors. Aust. J. Crop Sci. 9, 1022–1034 (2015)

    Google Scholar 

  32. Lo Bianco, M., Grillo, O., Cañadas, E., Venora, G., Bacchetta, G.: Inter and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques. Plant Biol. 19(2), 183–190 (2017a)

    Article  Google Scholar 

  33. Lo Bianco, M., Grillo, O., Escobar Garcia, P., Mascia, F., Venora, G., Bacchetta, G.: Morpho-colorimetric characterisation of Malva alliance taxa by seed image analysis. Plant Biol. 19(1), 90–98 (2017b)

    Article  Google Scholar 

  34. Loddo, A., Di Ruberto, C.: On the efficacy of handcrafted and deep features for seed image classification. J. Imaging (2021). https://doi.org/10.3390/jimaging7090171

    Article  Google Scholar 

  35. Loddo, A., Loddo, M., Di Ruberto, C.: A novel deep learning based approach for seed image classification and retrieval. Comput. Electron. Agric. 187, 106269 (2021)

    Article  Google Scholar 

  36. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)

    Article  Google Scholar 

  37. Orrù, M., Grillo, O., Lovicu, G., Venora, G., Bacchetta, G.: Morphological characterisation of Vitis vinifera L. seeds by image analysis and comparison with archaeological remains. Veg. Hist. Archaeobot. 22(3), 231–242 (2013)

    Article  Google Scholar 

  38. Orrù, M., Grillo, O., Venora, G., Bacchetta, G.: Computer vision as a method complementary to molecular analysis: grapevine cultivar seeds case study. C.R. Biol. 335(9), 602–615 (2012)

    Article  Google Scholar 

  39. Peng, J., Li, Q., Abd El-Latif, A.A., Niu, X.: Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics. Multimed. Tools Appl. 74(13), 4469–4486 (2015)

    Article  Google Scholar 

  40. Piras, F., Grillo, O., Venora, G., Lovicu, G., Campus, M., Bacchetta, G.: Effectiveness of a computer vision technique in the characterization of wild and farmed olives. Comput. Electron. Agric. 122, 86–93 (2016)

    Article  Google Scholar 

  41. Przybylo, J., Jablonski, M.: Using deep convolutional neural network for oak acorn viability recognition based on color images of their sections. Comput. Electron. Agric. 156, 490–499 (2019)

    Article  Google Scholar 

  42. Putzu, L., Ruberto, C.D., Fenu, G.: A mobile application for leaf detection in complex background using saliency maps. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D.C., Scheunders, P. (Eds.) Advanced Concepts for Intelligent Vision Systems—17th International Conference, ACIVS 2016, Lecce, Italy, October 24–27, 2016, Proceedings, Lecture Notes in Computer Science, vol. 10016, pp. 570–581 (2016)

  43. Rebetez, J., Satizábal, H.F., Mota, M., Noll, D., Büchi, L., Wendling, M., Cannelle, B., Perez-Uribe, A., Burgos, S.: Augmenting a convolutional neural network with local histograms: a case study in crop classification from high-resolution UAV imagery. In: Proceedings of ESANN 2016, European Symposium on Artifical Neural Networks, Computational Intelligence and Machine Learning, 27–29 April 2016, Bruges, Belgium, p. 6 (2016)

  44. Remeseiro, B., Mendonça, A.M., Campilho, A.: Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation. Vis. Comput. 37(6), 1247–1261 (2021)

  45. Sabato, D., Esteras, C., Grillo, O., Picó, B., Bacchetta, G.: Seeds morpho-colourimetric analysis as complementary method to molecular characterization of melon diversity. Sci. Hortic. 192, 441–452 (2015)

    Article  Google Scholar 

  46. Sarigu, M., Grillo, O., Bianco, M.L., Ucchesu, M., dHallewin, G., Loi, M.C., Venora, G., Bacchetta, G.: Phenotypic identification of plum varieties (Prunus domestica L.) by endocarps morpho-colorimetric and textural descriptors. Comput. Electron. Agric. 136, 25–30 (2017)

    Article  Google Scholar 

  47. Sau, S., Ucchesu, M., Dondini, L., Franceschi, P.D., dHallewin, G., Bacchetta, G.: Seed morphometry is suitable for apple-germplasm diversity-analyses. Comput. Electron. Agric. 151, 118–125 (2018)

    Article  Google Scholar 

  48. Sau, S., Ucchesu, M., dHallewin, G., Bacchetta, G.: Potential use of seed morpho-colourimetric analysis for Sardinian apple cultivar characterisation. Comput. Electron. Agric. 162, 373–379 (2019)

    Article  Google Scholar 

  49. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf Image classification. In: Computational Intelligence and Neuroscience (2016)

  50. Terral, J.F., Tabard, E., Bouby, L., Ivorra, S., Pastor, T., Figueiral, I., Picq, S., Chevance, J.B., Jung, C., Fabre, L., et al.: Evolution and history of grapevine (Vitis vinifera) under domestication: new morphometric perspectives to understand seed domestication syndrome and reveal origins of ancient European cultivars. Ann. Bot. 105(3), 443–455 (2010)

    Article  Google Scholar 

  51. Ucchesu, M., Orrù, M., Grillo, O., Venora, G., Usai, A., Serreli, P.F., Bacchetta, G.: Earliest evidence of a primitive cultivar of Vitis vinifera L. during the Bronze Age in Sardinia (Italy). Veg. Hist. Archaeobotany 24(5), 587–600 (2015)

    Article  Google Scholar 

  52. Ucchesu, M., Orrù, M., Grillo, O., Venora, G., Paglietti, G., Ardu, A., Bacchetta, G.: Predictive method for correct identification of archaeological charred grape seeds: support for advances in knowledge of grape domestication process. PLoS ONE 11(2), e0149814 (2016)

    Article  Google Scholar 

  53. Ucchesu, M., Sarigu, M., Del Vais, C., Sanna, I., dHallewin, G., Grillo, O., Bacchetta, G.: First finds of Prunus domestica L. in Italy from the Phoenician and Punic periods (6th-2nd centuries BC). Veg. Hist. Archaeobot. 26(5), 539–549 (2017)

    Article  Google Scholar 

  54. Vale, A.M.P.G., Ucchesu, M., Ruberto, C.D., Loddo, A., Soares, J.M., Bacchetta, G.: A new automatic approach to seed image analysis: from acquisition to segmentation (2020). arXiv:2012.06414

  55. Zhang, J., Wang, C., Li, C., Qin, H.: Example-based rapid generation of vegetation on terrain via CNN-based distribution learning. Vis. Comput. 35(6), 1181–1191 (2019)

    Article  Google Scholar 

  56. Zhu, H., Yang, L., Fei, J., Zhao, L., Han, Z.: Recognition of carrot appearance quality based on deep feature and support vector machine. Comput. Electron. Agric. 186, 106185 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, 09124, Cagliari, Italy

    A. Loddo & C. Di Ruberto

  2. Escola Agrícola de Jundiaí (EAJ), Universidade Federal do Rio Grande do Norte (UFRN), Rodovia RN 160, Km 03, Macaíba, RN, CEP 59280-000, Brazil

    A. M. P. G. Vale

  3. Centro Conservazione Biodiversità (CCB), Dipartimento di Scienze della Vita e dell’Ambiente (DiSVA), Università degli Studi di Cagliari, Viale S. Ignazio da Laconi 13, 09123, Cagliari, Italy

    M. Ucchesu & G. Bacchetta

  4. Hortus Botanicus Karalitanus (HBK), Università degli Studi di Cagliari, Viale S. Ignazio da Laconi 11, 09123, Cagliari, Italy

    G. Bacchetta

  5. Universidade Federal do Rio Grande do Norte (UFRN), Rodovia RN 160, Km 03, CEP 59280-000, Macaíba (RN), Brasil

    J. M. Soares

Authors
  1. A. Loddo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Di Ruberto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. M. P. G. Vale
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Ucchesu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. M. Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. G. Bacchetta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Loddo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loddo, A., Di Ruberto, C., Vale, A.M.P.G. et al. An effective and friendly tool for seed image analysis. Vis Comput 39, 335–352 (2023). https://doi.org/10.1007/s00371-021-02333-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02333-w

Keywords

Search

Navigation