Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin

Leikun Yin, Rahul Ghosh, Chenxi Lin, David Hale, Christoph Weigl, James Obarowski, Junxiong Zhou, Jessica Till, Xiaowei Jia, Nanshan You, Troy Mao, Vipin Kumar, Zhenong Jin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Cashews are grown by over 3 million smallholder farmers in >40 countries worldwide as a principal source of income. Expanding the area of cashew plantations and increasing productivity are critical to improving the livelihood of many smallholder communities. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. Expansion of the cashew industry is thus an essential economic driver and a governmental priority in Benin. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, two newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first-of-its-kind national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations with 2.4-m multi-temporal Planet Basemaps from 2019 to 2021, which can fully capture texture information from discriminative time steps during a growing season. The U-Net model was employed to map the distribution of cashew plantation with 0.5-m mono-temporal aerial imagery in 2015, which can achieve accurate and fast predictions even with limited training data. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy over 85% based on 1400 ground truth point samples from each year. The CASTC model achieved an overall accuracy of 76% based on 348 ground truth samples of planting density. We found that the cashew area in Benin has almost doubled to 519 ± 20 kha from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 55%. Only about half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape, which can help efficiently allocate limited training and nursery resources for sustainable agricultural development.

Original languageEnglish (US)
Article number113695
JournalRemote Sensing of Environment
Volume295
DOIs
StatePublished - Sep 1 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.

Keywords

  • Cashew plantation
  • Deep learning
  • Planet Basemaps
  • Smallholder agriculture
  • Tree crop mapping

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