# Leveraging Artificial Intelligence Techniques for Smart Palm Tree Detection: A Decade Systematic Review

arXiv: https://arxiv.org/abs/2209.05282 · HTML: https://mlsanity.com/paper/2209.05282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Novel CNN-LSTM-based Approach to Predict Urban Expansion](https://mlsanity.com/paper/2103.01695/) (2021)
- [Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images](https://mlsanity.com/paper/2005.05269/) (2020)
- [An improved tile-based scalable distributed management model of massive high-resolution satellite images](https://mlsanity.com/paper/2105.04731/) (2021)

## Built on by (most cited first, 1 of 1)

- [Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil](https://mlsanity.com/paper/2306.16862/) (2023)

## Abstract (arXiv)

Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries' economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for various stakeholders; it helps in yield estimation and examination to ensure better crop quality and prevent pests, diseases, better irrigation, and other potential threats. Despite their importance, this information is still challenging to obtain. This study systematically reviews research articles between 2011 and 2021 on artificial intelligence (AI) technology for smart palm tree detection. A systematic review (SR) was performed using the PRISMA approach based on a four-stage selection process. Twenty-two articles were included for the synthesis activity reached from the search strategy alongside the inclusion criteria in order to answer to two main research questions. The study's findings reveal patterns, relationships, networks, and trends in applying artificial intelligence in palm tree detection over the last decade. Despite the good results in most of the studies, the effective and efficient management of large-scale palm plantations is still a challenge. In addition, countries whose economies strongly related to intelligent palm services, especially in North Africa, should give more attention to this kind of study. The results of this research could benefit both the research community and stakeholders.