Learning-Based Macronutrient Detection Through Plant Leaf

The paper proposes a deep learning framework using two deep learning architectures, Keras and Pytorch to analyze the three macronutrients present in the plants basically nitrogen (N), phosphorous (P), potassium (K), i.e., NPK by the convolutional neural network (CNN). Agriculture is the backbone for the economy of a country, especially in the developing nations. Demand for food increases with an increase in population. To meet the increasing need for food, farmers need to maximize the productivity and balance the economy to reduce the losses. The plants require various minerals and nutrients for healthy growth and fruit development. Plant nutrients should be in proper proportion to keep plant healthier and less susceptible to pests. The nutrient analysis can be done by two techniques invasive and non-invasive techniques with their own advantages and disadvantages. Invasive or traditional methods are time-consuming and are costly, whereas non-invasive methods have proved its significance in recent years. The proposed methods are cost-effective and consume less time compared to conventional methods. The proposed framework provides an accuracy of 91% using Keras and 95% using Pytorch.