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Machine learning based object-level crop classification of PlanetScope data at South India Basin

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Abstract

Crop classification is of great significance as it opens doors to agricultural inventory and research related to crop mapping, crop yield, and economic analysis. In most studies to date, openly accessible satellites like Landsat, Sentinel, and MODIS are generally used. These satellites suffer from low spatial resolution as well as high revisit rates. Further, the classification in these studies is carried out at pixel level, which is not reliable as per-pixel spectral analysis often gives ambiguous results. Thus, this study proposes the use of high-resolution temporal PlanetScope imagery with an average spatial resolution of 3.7 m for object-based image analysis (OBIA) at the Southern Indian basin at the River Krishna. This area is abundant with a variety of crops like paddy, chilli, maize, lily, colocasia, curry leaves, mint, bhindi, banana, betel leaves, and sugarcane. The latest available ground truth data was from 2017 and hence, this has been used. Despite this data being a year older than the one used in a previous study, good and commendable accuracies were still produced by this work. The machine learning (ML) modelling and evaluation was carried out in Python while Google Earth Engine (GEE) was used as the primary platform for feature extraction, dataset preparation, and visualization of the final crop-classified image. For the purpose of object-based classification, this paper puts forward Support Vector Machines (SVM). To provide a comparative analysis to justify the performance of modelled SVM, several other ML algorithms like Convolution Neural Networks (CNN), Random Forest (RF), Artificial Neural Network (ANN), and Bayes Classifier were then modelled for this methodology in this study area. The results show that SVM performed best, with a high accuracy of 94.3%. All other algorithms modelled showed less accuracy comparatively, but were still above 75%.

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Data availability

PlanetScope dataset used in this study was accessed at https://www.planet.com/explorer.

(https://www.planet.com/markets/education-and-research/).

Data is available as ready-to-use products with the necessary sensor, radiometric calibration and ortho-rectification has been done.

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Acknowledgements

The authors express sincere gratitude to the Koneru Lakshmaiah Education Foundation for supporting this work. The authors would also like to thank the (1) National Remote Sensing Centre (NRSC) and Bhuvan (https://bhuvan.nrsc.gov.in/home/index.php) for providing ground truth data (2) Planet Inc.(https://www.planet.com/products/planet-imagery/) for providing PlanetScope dataset. We also thank Google Earth Engine for providing the platform to load dataset, compute indices and analyse the imageries.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, AP, India, 522502

    D. Bhavana, N. Likhita, G. V. Madhumitha & D. Venkata Ratnam

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  1. D. Bhavana
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  2. N. Likhita
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  3. G. V. Madhumitha
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  4. D. Venkata Ratnam
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Contributions

"All authors contributed to the study conception and design. Material preparation and data collection were performed by [Likhita N] and [GV Madhumitha] and algorithm implementation and result analysis was performed by [Dr D Bhavana] and [Dr D Venkata Ratnam]. The first draft of the manuscript was written by [Likhita N] and [GV Madhumitha]. All authors read and approved the final manuscript.”

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Correspondence to D. Bhavana.

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The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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The authors declare no competing interests.

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Communicated by: H. Babaie

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Bhavana, D., Likhita, N., Madhumitha, G.V. et al. Machine learning based object-level crop classification of PlanetScope data at South India Basin. Earth Sci Inform 16, 91–104 (2023). https://doi.org/10.1007/s12145-022-00922-4

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