Badril Hisham Abu Bakar
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Jusnaini Muslimin
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Muhammad Naim Fadzli Abd. Rani
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Mohammad Aufa Mhd Bookeri
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Mohd. Taufik Ahmad
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The standard practice among rice farmers in Malaysia is to apply fertilizer using a single application rate for the whole field. However, fertility conditions vary across the field. The excess use of fertilizer leads to increased input cost and can be damaging to the environment. The focus of this research was to develop a method to apply fertilizer on-the-go while sensing the crop nutrient status of rice plants. A machine learning approach was used to develop a crop nitrogen status prediction model. The model used spectral data from an active canopy reflectance sensor and several vegetation indices as inputs. The model was then incorporated into an on-the-go variable rate fertilizer application system. System performance was then evaluated in the field. The results from this work showed that the model had and accuracy of 83% in classifying the nitrogen status of the rice plants. The results also showed that our method was able to save up to 20% fertilizer use while maintaining yield. These findings are important for large estate farmers who are looking to increase productivity and efficiency.
Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery
