Using Artificial Intelligence (AI) to Combine Visible Near Infrared (Vis-NIR) Spectroscopy With Lab-Based Techniques for Predicting Nitrogen Levels in Leaves to Enhance Fertilizer Application in Banana Farming

Le résumé fourni par la source

The current method for estimating fertilizer application for crops such as bananas involves removing a leaf or several leaves from the plant and sending them to a lab for chemical analysis. While this method is highly accurate, the process is slow and cost prohibitive for measuring abundant samples to make a well characterized evaluation of a farm. Using cloud based artificial intelligence (AI) to create and monitor predictive models for quantitative nitrogen could allow nonexperts to utilize in a practical way the power of Vis-NIR and predictive quantitation; A handheld field portable Vis-NIR instrument was used in combination with a cloud-based AI-based automated workflow along with initial lab assays to create near real-time predictions in the field for quantitative nitrogen; This study showed the RSQ was 0.9 and the MAE was 0.08 % nitrogen. The high value of the RSQ indicates an excellent correlation of the % nitrogen assay values and the spectral scans collected by the Vis-NIR instrument. The MAE was lower than the expected method error from the lab, which was 0.35 % with a 99 % confidence interval measure; These findings suggest that AIenabled quantitative modeling for % nitrogen prediction in banana leaves of the Cavendish variety is possible. Combined with a cloud-based mapping solution, banana growers could make well characterized evaluations of their crops.

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