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.
