Analysis of IoT-Based Smart Crop Disease Detection System for Farmers in Gombe LGA, Gombe State
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- NGN 5000
Background of the Study :
Crop diseases are a major threat to agricultural productivity, significantly affecting the livelihoods of farmers in regions like Gombe LGA, Gombe State. Traditional methods of disease detection are often slow, labor-intensive, and dependent on the farmer’s expertise, which can delay critical interventions. IoT-based smart crop disease detection systems offer a revolutionary solution by using sensor networks, imaging technology, and machine learning algorithms to identify disease symptoms in real time. This study proposes the design and analysis of an IoT-based system that continuously monitors tomato and other crop fields for early signs of disease. Sensors, including high-resolution cameras and environmental sensors, will be deployed across fields to capture data on leaf color, moisture levels, and temperature, which are key indicators of crop health (Ibrahim, 2023). The collected data will be transmitted wirelessly to a centralized platform where machine learning models analyze the information and detect early signs of crop diseases. Alerts and recommendations for remedial actions will be sent to farmers via mobile applications, allowing for prompt and targeted interventions. Prior research has demonstrated that early detection can significantly reduce crop losses and improve yield (Olu, 2024). This study aims to evaluate the accuracy, reliability, and cost-effectiveness of the IoT-based detection system, considering the unique environmental conditions and resource constraints faced by farmers in Gombe LGA. The project will include field trials to assess the system’s performance and gather feedback from end-users. Ultimately, the research seeks to provide a scalable, cost-effective solution that enhances disease management practices, reduces economic losses, and contributes to sustainable agriculture in resource-limited settings (Adeniyi, 2025).
Statement of the Problem :
Farmers in Gombe LGA face significant challenges in managing crop diseases due to delayed detection and limited access to expert diagnostics. Traditional disease identification methods, which rely on manual inspection and visual assessment, are often inaccurate and slow, leading to late interventions and severe crop losses. The lack of a real-time, automated monitoring system exacerbates these issues, as farmers are unable to detect early signs of disease, resulting in widespread infection and reduced yields. Additionally, limited financial and technical resources hinder the adoption of advanced diagnostic tools in rural settings. The absence of an integrated system that can continuously monitor environmental and crop health parameters means that disease outbreaks are not addressed promptly, further impacting agricultural productivity and farmer income. There is an urgent need for an IoT-based smart crop disease detection system that provides real-time data, early alerts, and actionable insights to enable timely and effective interventions. This study aims to address these problems by developing a system that leverages advanced sensor technology and machine learning to detect disease symptoms accurately. By comparing the performance of the IoT system with conventional methods, the research intends to demonstrate its potential to reduce crop losses, improve yields, and promote sustainable farming practices in resource-constrained environments (Ibrahim, 2023; Olu, 2024).
Objectives of the Study:
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To design an IoT-based crop disease detection system that monitors key indicators in real time.
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To implement machine learning algorithms for accurate disease identification.
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To evaluate the system’s impact on reducing crop losses and improving yield.
Research Questions:
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How effective is the IoT-based system in early detection of crop diseases?
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What improvements in yield and reduction in crop losses can be achieved?
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How do farmers perceive the usability and effectiveness of the system?
Significance of the Study :
This study is significant as it develops an IoT-based smart crop disease detection system that enhances agricultural productivity by enabling early intervention. The system’s real-time monitoring and automated disease detection reduce crop losses and promote sustainable farming practices, offering a scalable solution for farmers in resource-limited settings (Adeniyi, 2025).
Scope and Limitations of the Study:
The study is limited to the analysis and evaluation of the smart crop disease detection system in selected farms in Gombe LGA, Gombe State, and does not extend to other crops or regions.
Definitions of Terms:
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Crop Disease Detection: The process of identifying signs of plant disease through monitoring and analysis.
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IoT (Internet of Things): A network of interconnected devices used for real-time data collection.
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Machine Learning: A branch of artificial intelligence that learns from data to make predictions or decisions.
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