A Deep Learning Framework for Precision Plant Nutrient Status on Different Crops

Abstract

Plant nutrient deficiencies can significantly impact agricultural productivity and crop quality, posing challenges for farmers in identifying and addressing these issues early. This project focuses on deep learning techniques as well as computer vision to detect nutrient deficiencies in different crops. network.We utilized a convolutional neural network (CNN) model with transfer learning, specifically the VGG16 architecture, as the foundation of our approach. The pre-trained base layers of VGG16 were frozen during initial training to retain learned features, and custom classification layers were integrated for optimal performance. To enhance model accuracy and robustness, extensive preprocessing techniques were employed, including background removal, normalization, and data augmentation. A publicly available dataset from Kaggle served as the primary source for training and validating the model. Our experiments demonstrated high classification accuracy, providing actionable insights for identifying nutrient deficiencies in crops. The broader impact of this work lies in its potential to improve agricultural productivity and crop management. By enabling early and accurate detection of nutrient deficiencies, this solution empowers farmers to take timely corrective actions, ensuring optimal crop health and reducing the risk of significant yield losses. This project underscores the transformative potential of AI in agriculture, paving the way for smarter and more sustainable farming practices.