Abstract:
Macro nutrient deficiency in paddy leaves is a
critical concern in agriculture that impacts crop yield, food
security, and sustainable farming. Addressing nutrient
deficiencies in paddy plants is vital for ensuring these
concerns. This research focuses on automating the detection
and classification of common macro-nutrient deficiencies,
specifically Nitrogen (N), Phosphorus (P), and Potassium (K).
Utilizing image processing techniques, the study identifies
distinct color patterns associated with each deficiency,
providing a non-invasive and efficient approach. The analysis
involves pixel ratio calculations within defined HSV color
ranges and threshold values. A modular workflow
encompasses preprocessing, horizontal partitioning, pixel
ratio computation, and deficiency classification. The
innovative methodology we introduced demonstrates
promising outcomes, achieving a 96% accuracy rate in
identifying nitrogen deficiency, along with 90% accuracy for
phosphorus deficiency and 92% accuracy for potassium
deficiency detection. While the methodology showcases
promise, certain limitations, such as the requirement for leaf
symmetry and single-deficiency identification, are recognized.
These findings lay the groundwork for more accurate and
automated nutrient deficiency detection, and the future work
aims to address the identified limitations and generalize the
solution for broader applications in real-world agricultural
settings.
