Paper Key : IRJ************078
Author: Khilendra Tumareki
Date Published: 12 Apr 2025
Abstract
Crop diseases pose a significant threat to global agricultural productivity, with the potential to cause severe economic losses, food shortages, and a negative environmental impact. In traditional agriculture, the early detection and timely management of plant diseases have been challenging tasks, often relying on manual inspection, expert knowledge, and reactive measures. However, as agriculture becomes more reliant on technology, innovative solutions have emerged to address these issues, offering more efficient, automated, and accurate methods for disease detection and management. One such advancement is the integration of Internet of Things (IoT) sensors with deep learning techniques, enabling the collection and analysis of both environmental and visual data. IoT sensors can provide real-time information about environmental conditions such as temperature, humidity, and soil pH, which play a crucial role in the development and spread of crop diseases. Meanwhile, image processing technologies, particularly those leveraging deep learning models like Convolutional Neural Networks (CNNs), allow for the automatic extraction of visual features from crop images, enabling the identification of disease symptoms on leaves or other parts of the plant. This research aims to explore and develop an advanced crop disease detection system that combines IoT-based environmental sensing with deep learning-based image analysis. The proposed system uses high-resolution images of crop leaves, captured through field-deployed cameras, and environmental data collected by IoT sensors installed in the crop field. By employing a self-attention mechanism within a deep neural network, the system can dynamically focus on disease-specific patterns in the data, improving classification accuracy. The significance of this study lies in its potential to provide farmers with a powerful, automated tool for the early detection of crop diseases. Early intervention can significantly reduce the economic burden of diseases and minimize the use of pesticides, leading to more sustainable agricultural practices. Additionally, this research contributes to the broader field of precision agriculture, where data-driven solutions are transforming the way crops are monitored, managed, and protected. This paper outlines the methodology for crop disease detection, explains the deep learning models and techniques employed, and discusses the experimental results obtained from the proposed system. It also highlights the challenges faced during development and the potential future directions for improving the system's accuracy, scalability, and applicability in real-world agricultural settings.
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