Object detection and tracking in Precision Farming: a systematic review

Mar Ariza-Sentís*, Sergio Vélez, Raquel Martínez-Peña, Hilmy Baja, João Valente

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)

Abstract

Object Detection and Tracking have gained importance in recent years because of the great advances in image and video analysis techniques and the accurate results these technologies are producing. Moreover, they have successfully been applied to multiple fields, including the agricultural domain since they offer real-time monitoring of the status of the crops and animals while counting how many are present within a field/barn. This review aims to review the current literature on Object Detection and Tracking within the field of Precision Farming. For that, over 300 research articles were explored, from which 150 articles from the last five years were systematically reviewed and analysed regarding the algorithms they implemented, the domain they belong to, the difficulties they faced, and which limitations should be tackled in the future. Lastly, it examines potential issues that this approach might have, for instance, the lack of open-source datasets with labelled data. The findings of this study indicate that Object Detection and Tracking are critical techniques to enhance Precision Farming and pave the way for robotization for the agricultural sector since they provide accurate results and insights on crop and animal management, and optimize resource allocation. Future work should focus on the optimal acquisition of the datasets prior to Object Detection and Tracking, along with the consideration of the biophysical environment of the farming scenarios.

Original languageEnglish
Article number108757
JournalComputers and Electronics in Agriculture
Volume219
DOIs
Publication statusPublished - Apr 2024

Keywords

  • Computer vision
  • Deep Learning
  • Precision agriculture
  • Precision Livestock farming
  • Smart farming

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