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An effective and friendly tool for seed image analysis

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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.

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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

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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

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  1. A. Loddo
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  2. C. Di Ruberto
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  3. A. M. P. G. Vale
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  4. M. Ucchesu
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Correspondence to A. Loddo.

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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

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