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Ag-IoT for crop and environment monitoring: Past, present, and future

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  • Chamara, Nipuna
  • Islam, Md Didarul
  • Bai, Geng (Frank)
  • Shi, Yeyin
  • Ge, Yufeng

Abstract

Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task.

Suggested Citation

  • Chamara, Nipuna & Islam, Md Didarul & Bai, Geng (Frank) & Shi, Yeyin & Ge, Yufeng, 2022. "Ag-IoT for crop and environment monitoring: Past, present, and future," Agricultural Systems, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:agisys:v:203:y:2022:i:c:s0308521x22001330
    DOI: 10.1016/j.agsy.2022.103497
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    References listed on IDEAS

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    2. Wu, Menglong & Xiong, Jiajie & Li, Ruoyu & Dong, Aihong & Lv, Chang & Sun, Dan & Abdelghany, Ahmed Elsayed & Zhang, Qian & Wang, Yaqiong & Siddique, Kadambot H.M. & Niu, Wenquan, 2024. "Precision forecasting of fertilizer components’ concentrations in mixed variable-rate fertigation through machine learning," Agricultural Water Management, Elsevier, vol. 298(C).

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