Black Gram Plant Nutrient Deficiency Classification in Combined Images Using Convolutional Neural Network

Abstract

Plant nutrient deficiency classification is vital for the agricultural industry to improve both the qualities and the quantities of crops. Computer vision and deep learning technologies, especially convolutional neural networks, perform an essential role in agricultural and biological sectors to solve the various kinds of complex problems. In this paper, we conducted the classification of the complete nutrient and six types of nutrient deficiency of black gram over the combined images of old leaf and young leaf. We found that the combined image supports more useful information than a single image. We accomplished the feature extraction process by taking the advantages of the deep pre-trained model to extract the features from the image automatically. Extracted features from ResNet50 deep pre-trained model are fed into three different classifiers as the input: (1) logistic regression, (2) support vector machine and (3) multilayer perceptron and compared the performance of these models. The multilayer perceptron models achieved superior performance than support vector machine and logistic regression by the accuracy of 88.33 %.