Classification of nitrogen deficiency for maize plants using deep learning algorithms on low-end android smartphones

Abstract

Maize comprises about one-fifth of the calories eaten in Sub-Saharan Africa. However, farmers in Sub-Saharan Africa have been unable to produce enough food for consumption. This is mostly due to a shortage of nutrients and the adoption of antiquated agronomic methods. Nitrogen deficiency or deficiency is a significant factor contributing to this poor yield of maize crop. Many smallholder farmers lack the necessary information to recognize this nitrogen deficit early on, when it is still reversible, before it damages their fields and maize yields. The purpose of this project is to determine a way for developing a mobile app for low-end Android phones that use a machine learning model to detect nitrogen insufficiency. The model is constructed by utilizing the Tensorflow and Keras libraries to train a pre-trained Single Shot Detector (SSD) Mobilenet model. Additionally, the approach takes advantage of Keras's built-in Image Augmentation algorithms to produce additional photos for our datasets. The model generated is 81 percent accurate. The Android application enables a smallholder farmer to maintain and analyze the soil health of several farms, as well as to determine the necessary fertilizer application to help rectify the nitrogen shortfall. Future directions in this field of study have also been highlighted for the benefit of interested scholars.