Title: Fruit disease detection using colour, texture and ANN: a sustainable approach for smart cities

Authors: Rohit Rastogi; Prateek Singh

Addresses: Department of CSE, ABES Engineering College Ghaziabad, UP, India ' Department of CSE, ABES Engineering College Ghaziabad, UP, India

Abstract: Timely and precise identification of fruit disease is crucial for preventing yield losses and ensuring crop quality. The purpose of this research focuses on the integration of colour texture analysis for automated detection using artificial neural networks (ANN). The combination of colour and texture features provide a comprehensive approach for visual characteristics of fruit surfaces, enhancing the accuracy and robustness of disease detection system. The proposed framework involves the following key steps: image acquisition, pre-processing, feature extraction, and classification using ANN. High-resolution images of fruit surfaces are captured using digital cameras or other imaging capturing devices. Pre-processing techniques including noise reduction and image enhancement are applied to ensure the quality of input data. The visual attributes of a fruit are represented through the computation of colour and texture features using methods like local binary patterns or grey level co-occurrence matrix. The study uses feature vectors to train an ANN to identify and classify fruit diseases based on colour and texture information.

Keywords: artificial neural networks; ANNs; training data; learning algorithms; classification metric; precision; agriculture; Python; OpenCV; TensorFlow; PyTorch; data augmentation; explainable AI; XAI; HTML; CSS; JavaScript; Streamlit.

DOI: 10.1504/IJAITG.2024.142183

International Journal of Agriculture Innovation, Technology and Globalisation, 2024 Vol.4 No.1, pp.63 - 96

Received: 06 Jan 2024
Accepted: 30 Apr 2024
Published online: 11 Oct 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article