Smart Farming Application: Leveraging AI, Soil Analysis, and Weather Data for Optimized Crop Cultivation and Sustainable Agriculture

Abstract

Today, in agriculture: the modern farmer struggles with both high crop yield maximization and also simultaneously achieving sustainable farming practices. This paper describes an artificial intelligence (AI) based soil analysis application coupled with real-time weather data to assist in crop cultivation for developing smart farming. The app has increasingly been used for crop search, as well besides the convenience of providing information on crops giving helpful hints on sowing season and fertilizer needs to tread water footprint exercises by knowing base fertilizer use rates upfront till market prices! Based on the GPS data, it will analyze soil test reports: NPK values, pH levels of the area with weather conditions around your local and develop personalized farming roadmaps giving you a perfect step-by-step guide starting from preparing your land to harvesting. This pests and diseases identifier also has an option to upload images of the pest and helps you find the correct pesticide. Furthermore, it suggests the crops which can be grown according to user location and season along with advised healthy farming practices required for long-lasting soil fertility. In agriculture, this demonstration points to a probable future where technology can be used for data-driven guidance that enables farmers on actionable information — an end-to-end solution tailored around the essentials of sustainable crop production.

Introduction

Agriculture is vital to global food supply but faces challenges like climate variability, soil degradation, pests, and outdated farming practices. Modern demands require smart, technology-driven solutions. This paper proposes a smart farming system that integrates artificial intelligence (AI), soil analysis, and GPS-based rainfall data to help farmers optimize crop production. The system personalizes crop recommendations using soil test results and current weather, provides pest identification via image recognition, updates real-time market prices, and suggests sustainable farming practices.

Related works highlight existing precision agriculture tools using AI for weather, soil, pest management, and crop recommendations. However, many tools focus on isolated functions rather than offering a comprehensive solution. This app aims to bridge that gap by integrating multiple features in one platform.

The methodology explains how users operate the app: from downloading and account setup to submitting soil reports, receiving personalized cultivation roadmaps, managing pests through image uploads, monitoring weather and market prices, and providing feedback. The app uses detailed datasets such as water footprints of various crops and seasonal cropping patterns to refine recommendations.

Results demonstrate the advantages of this AI-driven approach over traditional farming, including better resource management (fertilizer, water), improved pest and disease control, weather-informed decisions, and enhanced market alignment. The system promotes sustainable practices, reduces environmental impact, and boosts crop yields and farmer profitability by enabling data-driven, personalized farming.

Conclusion

The integration of machine literacy( ML) into agrarian practices through the smart husbandry operation represents a significant advancement in ultramodern husbandry. By employing the power of data analytics and AI, the operation will give growers with customized recommendations for crop civilization, resource operation, pest and complaint control, and request analysis. This exploration demonstrates that ML can effectively address numerous of the challenges associated with traditional husbandry, including hamstrung resource use, pest and complaint operation difficulties, rainfall unpredictability, and request pricing issues. The results of the perpetration of smart husbandry have shown notable advancements in tilling effectiveness and productivity in former exploration studies too. growers have endured increased crop yields, reduced input costs, and enhanced decision- making capabilities. The app’s individualized roadmaps and real- time data integration will have major significance in optimizing agrarian practices and promoting sustainability. Still, challenges similar as data integration, stoner relinquishment, and connectivity issues remain. Addressing these challenges is pivotal for maximizing the app’s impact and icing its wide relinquishment. Despite these hurdles, the positive issues of this exploration punctuate the transformative eventuality of ML in husbandry. By continuing to introduce and upgrade agrarian technologies, we can further enhance husbandry practices, support sustainable development, and contribute to the unborn adaptability of the agrarian sector.

References

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Copyright

Copyright © 2025 Sanskriti Sahu, Tejasva Ukey, Jagriti Singh, Ashutosh Yadav, Samiksha Shukla. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.