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@article{187636,
        author = {RAMESHKUMAR S and Keerthika S and Vennila C and Arthi D and Neelavathi T},
        title = {Design and Development of an AI-Based Crop Recommendation and Farming Assistance Web Application for Tomato},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5767-5771},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187636},
        abstract = {Tomato cultivation is a critical component of global agriculture but faces significant challenges from unpredictable weather, pest infestations, soil degradation, and inefficient resource management. This paper presents the design and development of a sustainable tomato cultivation system that leverages artificial intelligence (AI) to provide data-driven decision support. The proposed web application integrates real-time data from soil sensors, weather forecasts, and crop health imaging to generate personalized recommendations for irrigation, fertilization, and pest control. By employing machine learning (ML) and convolutional neural networks (CNN), the system facilitates early disease detection and resource optimization, promoting precision agriculture. The platform features a user-friendly interface with an interactive dashboard for data visualization, a smart camera for plant disease analysis, and an AI-powered chatbot for real-time assistance. Implementation results indicate that the system can enhance crop yield, reduce operational costs, and minimize environmental impact, thereby supporting the transition towards climate-resilient and sustainable farming practices.},
        keywords = {Artificial Intelligence, Precision Agriculture, Tomato Cultivation, Crop Recommendation, Disease Detection, Sustainable Farming, Web Application.},
        month = {November},
        }