2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 1 , PP: 78-94, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ashish Patel 1 , Richa Mishra 2 , Aditi Sharma 3 *
Doi: https://doi.org/10.54216/FPA.170106
The development and productivity of maize, an important crop worldwide, may be stunted by several nutritional deficiencies. If we want to increase maize output, we need to find these problems quickly. This study suggests a thorough method for identifying nutritional deficits in maize plants by analyzing leaf photos. Our approach combines deep learning algorithms with conventional machine learning methods to analyze and extract information from these pictures. The four types of nutritional deficiencies that were examined are zinc (Zn), potassium (K), nitrogen (N), and phosphorus (P). The standard machine learning method uses Gabor, Discrete Wavelet Transform, Local Binary Pattern, and Gray-Level Co-occurrence Matrix (GLCM). Then, classification is done using algorithms like Support Vector Machine (SVM), Decision Tree, and Gradient Boosting. According to our experimental data, machine-learning algorithms successfully diagnose nutritional deficits in maize plants. The results of this study highlight the promise of machine learning algorithms for improving agricultural yields via better plant nutrition management. Farmers and agricultural specialists may greatly benefit from automated image analysis that can identify nutritional deficits in maize plants quickly and correctly. This technology has the potential to contribute to the sustainability and security of food on a worldwide scale.
Nutrient deficiency , Maize plant , Deep learning , Image classification , Agriculture
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