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The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

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  • Mwinuka, Paul Reuben
  • Mbilinyi, Boniface P.
  • Mbungu, Winfred B.
  • Mourice, Sixbert K.
  • Mahoo, H.F.
  • Schmitter, Petra

Abstract

This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources.

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  • Mwinuka, Paul Reuben & Mbilinyi, Boniface P. & Mbungu, Winfred B. & Mourice, Sixbert K. & Mahoo, H.F. & Schmitter, Petra, 2021. "The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)," Agricultural Water Management, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:agiwat:v:245:y:2021:i:c:s0378377420321314
    DOI: 10.1016/j.agwat.2020.106584
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    References listed on IDEAS

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    1. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    2. Pappalardo, S. & Consoli, S. & Longo-Minnolo, G. & Vanella, D. & Longo, D. & Guarrera, S. & D’Emilio, A. & Ramírez-Cuesta, J.M., 2023. "Performance evaluation of a low-cost thermal camera for citrus water status estimation," Agricultural Water Management, Elsevier, vol. 288(C).
    3. Rubaiya Binte Mostafiz & Ryozo Noguchi & Tofael Ahamed, 2021. "Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices," Land, MDPI, vol. 10(2), pages 1-26, February.
    4. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
    5. Mwinuka, Paul Reuben & Mourice, Sixbert K. & Mbungu, Winfred B. & Mbilinyi, Boniphace P. & Tumbo, Siza D. & Schmitter, Petra, 2022. "UAV-based multispectral vegetation indices for assessing the interactive effects of water and nitrogen in irrigated horticultural crops production under tropical sub-humid conditions: A case of Africa," Agricultural Water Management, Elsevier, vol. 266(C).

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