Classification of Macronutrient Deficiencies in Maize Plant Using Machine Learning

Leena N, K. K. Saju


Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in non-destructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets.


deep learning with auto encoders, feature extraction, k nearest neighbors, nutritional deficiency, support vector machines,

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578