AI Model for Identification of Micro-Nutrient Deficiency in Banana Crop

Abstract

ABSTRACT This study offers a sophisticated convolutional neural network (CNN) model that uses leaf image analysis to identify micronutrient deficits in banana crops. For the best crop growth and output, proper nutrition is necessary, and deficits in important nutrients can negatively affect the productivity and health of plants. In order to tackle this issue, we have created a customized CNN model that uses detailed leaf photos to identify and categorize different nutrient shortages. A large dataset of banana leaves displaying various deficiency signs was employed in the study to train and assess the model. In order to improve feature extraction and classification skills and enable accurate identification of nutrient-related disorders, the CNN architecture was meticulously adjusted. Keywords: Banana Crop Micronutrient Deficiency, Convolutional Neural Network (CNN), Leaf Image Analysis, Image-Based Nutrient Diagnosis, Deep Learning in Agriculture, Plant Nutrient Classification, Precision Agriculture, Agricultural Image Processing, CNN-Based Deficiency Detection, AI-Driven Crop Management

Plant phenotyping relevance

バナナ葉画像から栄養欠乏という植物状態を推定するCNN手法の開発・評価が研究の中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper describes a CNN for banana leaf micronutrient deficiency classification but provides no public dataset, image collection, code, model checkpoint, or supplement with availability statements or URLs. No qualifying paper-specific assets are present, and no allowed URLs exist to cite.

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