Irrigated corn grain yield prediction in Florida using active sensors and plant height

Leitão, Diego Arruda Huggins de Sá; Sidhu, Sudeep S.; Griffin, Winniefred D.; Ahmad, Uzair; Sharma, Lakesh K.

doi:10.1016/j.atech.2023.100276

Cited by 3 publications

References 45 publications

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The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

Netto‐Ferreira,

de Sá Leitão,

Griffin

et al. 2024

Agrosystems Geosci & Env

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A sorghum sudangrass (SSG) cover crop grown after a cash crop could take up residual nitrogen (N) before it is lost. As in‐field monitoring of SSG properties is laborious, predicting biomass and N concentrations with spectral sensors could be useful. At two sites in Live Oak, Florida, we evaluated the response of SSG to residual N from previous N fertilization and the performance of handheld and satellite sensors in estimating SSG properties. We quantified aboveground biomass, plant N, leaf greenness (NDVI), net potential N mineralization (PNM), and soil permanganate oxidizable carbon (POXC). Residual N did not affect SSG properties, PNM was highest at the highest N input rate in one site, and soil POXC was correlated with SSG properties (biomass and plant N). NDVI measured from a handheld sensor better predicted SSG properties than satellite imagery in these small plots, suggesting a greater potential to be a useful management tool.

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The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

Netto‐Ferreira,

de Sá Leitão,

Griffin

et al. 2024

Agrosystems Geosci & Env

View full text Add to dashboard Cite

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Yield and plant height predictions of irrigated maize through unmanned aerial vehicle in North Florida

Arruda Huggins de Sá Leitão,

Sharma,

Singh

et al. 2023

Computers and Electronics in Agriculture

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A prediction model for grain yield in Henan province based on BP neural network

Xu,

Yuan

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Henan Province is an important agricultural province in China, and its food production is crucial for meeting the country's food needs and ensuring food security. This article establishes a prediction model for grain yield in Henan Province based on BP neural network. Six indicators are selected as input variables, including total power of agricultural machinery, effective irrigation area, converted amount of agricultural fertilizer application, pesticide usage, sowing area of grain crops, and rural electricity consumption. Grain yield is used as output variable. The experimental results show that the error rate of the BP neural network prediction model in the training and validation stages is controlled within 3%, indicating that the model has good prediction performance and is helpful for the government to formulate agricultural planning and agricultural production management strategies.

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Irrigated corn grain yield prediction in Florida using active sensors and plant height

Cited by 3 publications

References 45 publications

The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

Yield and plant height predictions of irrigated maize through unmanned aerial vehicle in North Florida

A prediction model for grain yield in Henan province based on BP neural network

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