Boron Deficiency Detection in Banana Leaves using Skip-Connected Convolutional Neural Network (SC-CNN)
DOI:
https://doi.org/10.12723/mjs/70.1Keywords:
Nutrient, Micronutrient, Deep Learning, Deficiency, Skip Connection, Machine Learning, BoronAbstract
Plants rely on a delicate balance of 16 essential nutrients to thrive, with macronutrients being crucial for robust growth, while micronutrients play a vital role despite being needed in smaller quantities. Insufficient nutrient levels can stunt plant growth, hinder flowering, and reduce fruit yield. Accurate diagnosis of these deficiencies is paramount for farmers to address issues effectively, ensuring the cultivation of nutrient-rich crops and maximizing yield. Bananas, a globally significant fruit crop known for its high nutritional value, require meticulous nutrient management to thrive. Micronutrients like Boron, are critical for maintaining hormonal equilibrium in banana plants, with deficiencies often manifesting visibly on leaves. This study proposes a deep-learning approach to detect Boron deficiencies in banana leaves. The developed CNN model with Skip Connections (CNNSC), comprising thirteen layers, outperforms established architectures like VGG16, DenseNet, and Inception V3. Evaluation metrics showcase the model’s effectiveness, achieving a remarkable accuracy of approximately 95%.
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