Mulberry Leaf Disease Detection Using CNN-Based Smart Android Application

Aim: 

        To develop an Android application for detecting diseases in mulberry leaves using deep learning and provide actionable insights like weather data analysis and fertilization recommendations.

Abstract:

          The health of mulberry leaves is essential for the success of sericulture, as these leaves serve as the primary food source for silkworms, whose silk production directly impacts the economy. However, mulberry plants are vulnerable to various diseases that can result in crop losses and reduced silk production. Identifying these diseases at an early stage can help prevent significant damage. Traditional methods of disease detection, which often involve manual inspection by farmers or agricultural experts, can be time-consuming, subjective, and prone to errors. To address these challenges, an Android-based application has been developed that leverages deep learning algorithms to analyze images of mulberry leaves and detect diseases effectively and efficiently.

         Furthermore, the app integrates an AI-powered question and answer system, providing instant responses to farmers’ queries related to farming practices, disease management, fertilization, and other relevant topics. By combining machine learning, weather data analysis, personalized recommendations, and government news updates, this Android-based solution offers a holistic approach to improving mulberry plant health, increasing productivity, and promoting sustainable sericulture practices. The system ultimately supports farmers in making informed decisions that enhance their productivity and resilience against environmental and market challenges.

Existing System:

         Manual inspection by experts or farmers is the primary method used to detect leaf diseases. Relies on experience and visual analysis, leading to inaccuracies. No integrated system to correlate weather data with disease prevalence or recommend fertilization schedules.

Disadvantages:

1.  Inaccuracy: Human error leads to incorrect identification and diagnosis.
2. Time-Consuming: Manual inspection is slow and unsuitable for large-scale farming.
3. Lack of Data Integration: Weather patterns and fertilization requirements are not considered.
4. Costly: Farmers may need to hire experts for regular inspections.

Proposed System:

        The application works by allowing farmers to capture images of the mulberry leaves using their smartphones. These images are then processed through a trained machine learning model, which has been fed with a large dataset of images labeled as healthy or diseased. The model, likely based on Convolutional Neural Networks (CNN), processes the image data and classifies the leaf as either healthy or affected by a specific disease, providing real-time diagnosis. The system’s high accuracy reduces the need for manual inspections, offering faster results and helping farmers detect problems at an earlier stage.

       In addition to disease detection, the app integrates real-time weather data, which plays a crucial role in disease development. Weather patterns such as humidity, temperature, and rainfall can influence the spread of diseases in plants. The application collects data from weather APIs, and by analyzing these patterns, it alerts the user to conditions that may be conducive to disease outbreaks, helping them take timely preventive actions.

       The app also provides customized fertilization recommendations based on the specific needs of the mulberry plants. Fertilization schedules and nutrient requirements can vary based on environmental factors such as soil type, weather, and disease presence. By analyzing these parameters, the application can suggest the optimal type and quantity of fertilizer, promoting healthy plant growth and improving the crop yield.

     Furthermore, predictive analytics is employed to forecast potential disease outbreaks based on current weather conditions and the historical spread of diseases in the region. This feature allows farmers to prepare in advance, implementing preventive measures to avoid disease outbreaks before they happen. By combining machine learning, weather data, and fertilization advice, this Android-based system provides a holistic solution to improving mulberry leaf health, thus boosting the overall productivity and sustainability of sericulture.

         By integrating an API to fetch live government news related to agriculture, farmers can easily access the latest updates that may affect their farming practices. The integration of government policies, such as crop insurance or price support, can help them take advantage of state-sponsored schemes. With AI-powered Q&A, farmers can have their doubts resolved instantly without needing to consult experts directly. The AI could be integrated with both the disease detection system (for crop health issues) and the fertilization recommendation system, offering seamless, personalized support.

Advantages: 

1. Precision: High accuracy in detecting diseases using machine learning algorithms.
2. Efficiency: Faster disease diagnosis compared to manual methods.
3. Data Integration: Combines weather data, disease analysis, and fertilization advice.
4. Cost-Effective: Reduces the dependency on experts, saving costs.
5. User-Friendly: Accessible through a smartphone application.