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Comparative analysis of texture descriptors in maize fields with plants, soil and object discrimination

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Abstract

Precision Agriculture aims to apply selective treatments and tasks at localized areas concerning crop fields. Robotized and autonomous tractors, equipped with perception, decision-making and actuation systems, can apply specific treatments as may be required. Correct plant identification through the perception system, including crops and weeds, is an important issue. Additionally, it is well known that, in autonomous vehicles, safety is a major challenge, where unexpected obstacles in the working area must be conveniently addressed in order to guarantee the security and the continuity of the process. The objective of this study was to design a tri-class Support Vector Machine classifier for identifying plants (crops and weeds), soil and objects in maize fields based on unsupervised learning. For this, a strategy for automatic sample selection was designed to obtain elements of the three involved classes for the training process. In this context, the identification of obstacles for safe navigation makes an important contribution. A comparative analysis of different texture descriptors and local patterns was carried out with the aim of determining the best for characterizing the classes under study; results have shown that the Speeded-Up Robust Features descriptor is the most appropriate to discriminate between plants, soil and objects. The development of an object detection algorithm for agricultural images proved the effectiveness of the tri-class classifier with an accuracy of 94.3%.

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Acknowledgements

The first author acknowledges The National Council of Science and Technology of Mexico (CONACyT) for the doctoral grant number 210282 to undertake doctoral studies. H. Sossa thanks CONACyT under call: Frontiers of Science (Grant Number 65) for the economic support. The research leading to these results has been funded by the European Unions Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 245986. We would like to express our thanks to the reviewers, especially to the Chief Editor, for their valued remarks, their comments and suggestions were fundamental for the improvement of this paper.

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Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, Complutense University, 28040, Madrid, Spain

    Yerania Campos & Gonzalo Pajares

  2. Instituto Politécnico Nacional – CIC, Av. Juan de Dios Bátiz, S/N, Col. Nueva Industrial Vallejo, 07738, Mexico, DF, Mexico

    Humberto Sossa

Authors
  1. Yerania Campos
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Correspondence to Yerania Campos.

Appendix: Textural features

Appendix: Textural features

See Tables 6, 7, and 8

Table 6 First-order features for a single pixel (x, y) in a grey scale image \(I_{GREY} (x,y);\,W(x,y)\) is a small vicinity, window of size \(\omega - by - \omega\), around (x, y) (Andrzej and Strzelecki 1998)
Full size table
Table 7 Run length matrix p: \(n_{r}\) is total number of runs and \(n_{p}\) is the number of pixels on the window/image
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Table 8 Grey level co-occurrence matrix (q)
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Campos, Y., Sossa, H. & Pajares, G. Comparative analysis of texture descriptors in maize fields with plants, soil and object discrimination. Precision Agric 18, 717–735 (2017). https://doi.org/10.1007/s11119-016-9483-4

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