Machine and deep learning for the prediction of nutrient deficiency in wheat leaf images

Abstract

Nutrient deficiency in wheat plants can lead to diseases and important losses in yield. These diseases can be visually detected on wheat leaf images. We perform the image classification as nutrient controlled or deficient, using a collection of 57 machine learning classifiers programmed in 4 different programming languages, applied on color texture features extracted from the images. We also use other 90 methods under the Caret automated R classification framework on the same features. Furthermore, we use 62 deep learning networks under three frameworks applied on the leaf images in three settings: trained from the scratch, fine-tuning of pretrained networks and classification of deep and shallow features extracted by deep networks. The radial basis function (RBF) neural network achieves the best performance, with kappa and accuracy of 57% and 81.2%, and with a low false positive rate (11.1%), while pretrained deep networks and classification of shallow features achieve 40% and 47%, respectively. Since nutrient deficiency is a continuous concept, ranging from 0% to 100%, and a sharp categorization into controlled and deficient may always be relative, these results identify the RBF network as an accurate approach for the detection of nutrient deficiency in wheat leaves.