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Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems

Author

Listed:
  • Brewer, K.
  • Clulow, A.
  • Sibanda, M.
  • Gokool, S.
  • Naiken, V.
  • Mabhaudhi, Tafadzwanashe

Abstract

Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m-2 , 39 µmol/m-2 , and 61.6 µmol/m-2 , respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m-2 and 69.6 µmol/m-2 , respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.

Suggested Citation

  • Brewer, K. & Clulow, A. & Sibanda, M. & Gokool, S. & Naiken, V. & Mabhaudhi, Tafadzwanashe, 2022. "Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems," Papers published in Journals (Open Access), International Water Management Institute, pages 1-14(3):518.
    • Handle: RePEc:iwt:jounls:h050903
    DOI: 10.3390/rs14030518
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    References listed on IDEAS

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    1. Adeleke Oluwole Salami & Abdul Kamara & Zuzana Brixiova, 2010. "Working Paper 105 - Smallholder Agriculture in East Africa: Trends, Constraints and Opportunities," Working Paper Series 242, African Development Bank.
    2. Luxon Nhamo & James Magidi & Adolph Nyamugama & Alistair D. Clulow & Mbulisi Sibanda & Vimbayi G. P. Chimonyo & Tafadzwanashe Mabhaudhi, 2020. "Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-18, July.
    3. Nhamo, Luxon & van Dijk, R. & Magidi, J. & Wiberg, David & Tshikolomo, K., 2018. "Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV [Unmanned Aerial Vehicle] capability," Papers published in Journals (Open Access), International Water Management Institute, pages 10(5):1-12..
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    1. Shaeden Gokool & Maqsooda Mahomed & Richard Kunz & Alistair Clulow & Mbulisi Sibanda & Vivek Naiken & Kershani Chetty & Tafadzwanashe Mabhaudhi, 2023. "Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    2. Colette de Villiers & Cilence Munghemezulu & Zinhle Mashaba-Munghemezulu & George J. Chirima & Solomon G. Tesfamichael, 2023. "Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    3. Jiangtao Ji & Nana Li & Hongwei Cui & Yuchao Li & Xinbo Zhao & Haolei Zhang & Hao Ma, 2023. "Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing," Agriculture, MDPI, vol. 13(5), pages 1-15, May.

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