Masters Thesis

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Browsing Masters Thesis by Author "Fazla Rabbi"

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    An integrated mobile app for rice disease detection and weather prediction
    (2024-05-30) Fazla Rabbi
    This project seeks to create a cutting-edge mobile application that combines the capacity for disease detection in rice crops and precise weather forecasting for agricultural areas. Rice is a staple crop for billions of people around the world, making agriculture a crucial economic sector. However, if these diseases are not promptly detected and treated, rice crops are vulnerable to a number of illnesses that can result in significant output losses. The success and growth of crops are significantly influenced by the weather as well. In order to provide rice farmers and other agricultural stakeholders with a complete solution, the suggested mobile app makes use of cutting-edge technology, such as machine learning and the integration of real-time weather data. The project's primary goals are as follows: 1. Rice Disease Detection: In order to recognize common illnesses impacting rice crops, such as blast, bacterial leaf blight, and sheath blight, the app will use picture recognition and machine learning algorithms. With the use of an app, users may take pictures of damaged rice plants using their mobile devices, and the program will instantly analyze and diagnose the situation, enabling farmers to take quick corrective action. 2. Weather Prediction: To deliver precise weather forecasts for particular agricultural areas, the app will link existing sources of weather data and apply cutting-edge predictive models. Farmers will be able to make informed decisions about planting, harvesting, and pest control thanks to this feature, which will contain data on temperature, humidity, precipitation, wind speed, and other pertinent elements. 3. Crop Management Insights: Based on disease detection and meteorological information, the app will provide actionable insights. Users will get advice on crop management techniques, such as when to apply pesticides, when to schedule irrigation, and how to rotate their crops. Losses will be reduced and crop yields will be optimized as a result. 4. User-Friendly Interface: Farmers with various degrees of technological ability will be able to utilize the mobile app because of its user-friendly layout. To reach a large user base, it will be accessible on both the Android and iOS platforms. 5. Data Privacy and Security: The app will use strong encryption and abide by data protection laws to guarantee the security and privacy of user data. This project intends to equip rice farmers with the resources and knowledge required to improve crop yield and sustainability by combining disease detection and weather forecasting in a single mobile application. Increased yields, a decrease in the use of pesticides, better resource management, and improved lives for rice farmers are all potential effects of the app. Keywords: Rice disease detection, mobile app, weather prediction, agriculture, machine learning, image recognition, crop management, agricultural technology, data privacy, sustainability.