Applied Intelligence and Computing
Simple Neural Network for Identifying Fruit and Leaf Disease in Apple and Mango
Authors: Gautam Dwivedi, John Heribert Nongkynrih and Sunita Warjri
Publishing Date: 08-11-2024
ISBN: 978-81-955020-9-7
Abstract
The paper presents a novel approach utilizing machine learning techniques, specifically a Simple Neural Network (SNN) algorithm, for automated detection of apple and mango fruit and leaf diseases. Through extensive data collection, preprocessing, and model development stages, the system achieves a remarkable 95% accuracy in classifying diseased instances within the dataset. Leveraging transfer learning with MobileNetV2 architecture and employing evaluation metrics like accuracy, the model demonstrates robust performance in distinguishing between healthy and diseased samples. Furthermore, the analysis of model performance over time reveals a progression from initial overfitting to eventual convergence, indicating the model's capacity for generalization to unseen data. This research significantly contributes to addressing challenges in agricultural productivity and food security by providing a scalable, efficient, and objective solution for early disease detection, thus empowering growers and experts to implement timely interventions and enhance crop health and yields.
Keywords
Automated detection, Machine learning, Fruit diseases, Identification, Leaf diseases, Smart Agricultural.
Cite as
Gautam Dwivedi, John Heribert Nongkynrih and Sunita Warjri, "Simple Neural Network for Identifying Fruit and Leaf Disease in Apple and Mango", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2024, pp. 81-90. https://doi.org/10.56155/978-81-955020-9-7-10