2020/03/05 by Anjaneya Teja Sarma Kalvakolanu, Kalvakolanu, Anjaneya Teja Sarma
Agricultural and Biological Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Date Palm Research Studies #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Leaf Properties and Growth Measurement #Smart Agriculture and AI #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2003.05379
openalex publication_date 2020/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Plant disease detection is a huge problem and often require professional help to detect the disease. This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants. The deep learning is done with the help of Convolutional Neural Network by performing transfer learning. The model is created using transfer learning and is experimented with both resnet 34 and resnet 50 to demonstrate that discriminative learning gives better results. This method achieved state of art results for the dataset used. The main goal is to lower the professional help to detect the plant diseases and make this model accessible to as many people as possible.