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@article{177326,
        author = {Pavitra Gupta and Jhalak Tomar and Iti Jain and Harshit Kumar},
        title = {Agri++ - A Virtual Crop Assistant},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1892-1895},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177326},
        abstract = {Agriculture is important for feeding people around the world, but farmers face many challenges like changing weather, poor soil quality, synthetic fertilizers which affect the crops badly. Traditional ways of agriculture do not work well, leading to unsatisfied farming.
This paper represents a Crop Prediction System that uses machine learning to help farmers in choosing the best crops for their land. The system considers factors like soil type (pH and nutrients), climate to suggest suitable crops. It uses advanced techniques with unique features such as Random Forest and Neural Networks to analyze past and current data.
Traditional farming doesn’t satisfy farmers as it gave less productivity and sometimes makes a huge loose in money and time. This paper improved productivity in farming and increased benefits.
The system is designed to work on a large scale and aimed to give more accurate results. It has an easy-to-use interface for entering and viewing data. It supports multiple languages, and provides extra feature like fertilizer suggestions and a chatbot for assistance. By including real-time weather and market prices, it helps farmers to make better decisions, reducing risks and increasing crop yields and productivity.
Various test cases shows that the system is highly accurate and performs better than traditional methods. It helps farmers use their resources wisely, reduces harm to the environment, improves their earnings and improves productivity. This approach supports sustainable farming and ensures a highly rich food supply.},
        keywords = {Crop Prediction, Machine Learning, Sustainable Agriculture, Soil Analysis, Precision Farming, Real-Time Data Integration.},
        month = {May},
        }