Agrovision: Smart Solutions for Modern Farming

Kunal More, Vishvesh Ghongade, Chinmay Asodekar, Prof. Shreeya Palkar and Shreyash Mandlik
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Kunal More: Department of Artficial Intelligence and Data Saience, Progerssive Education Society’s Modern College of Engineering, Shivaji Nagar, Pune, Maharashtra, India
Vishvesh Ghongade: Department of Artficial Intelligence and Data Saience, Progerssive Education Society’s Modern College of Engineering, Shivaji Nagar, Pune, Maharashtra, India
Chinmay Asodekar: Department of Artficial Intelligence and Data Saience, Progerssive Education Society’s Modern College of Engineering, Shivaji Nagar, Pune, Maharashtra, India
Prof. Shreeya Palkar: Department of Artficial Intelligence and Data Saience, Progerssive Education Society’s Modern College of Engineering, Shivaji Nagar, Pune, Maharashtra, India
Shreyash Mandlik: Department of Artficial Intelligence and Data Saience, Progerssive Education Society’s Modern College of Engineering, Shivaji Nagar, Pune, Maharashtra, India

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 4, 945-955

Abstract: AgroVision is a mobile-centric artificial intelligence- driven platform, particularly designed to enhance the efficiency and sustainability of contemporary agriculture operations, specif- ically focusing on small-scale farmers in resource-constrained areas. AgroVision offers personalized crop prescriptions using soil pH, moisture, and nutrient levels, as well as for weed and crop detection through the YOLOv8 algorithm. In contrast to hardware-locked proprietary agricultural innovations, AgroVi- sion can execute seamlessly on mobile devices via a Flutter app, allowing farmers to take pictures of their fields and input soil data directly. From this analysis, the insights provided by AgroVision are tailored to the user so that decisions can be made regarding maximized crop yield, deepening ecological impact, and ecological footprint minimization. While the development team faced challenges with low computational power and a lack of varied training data, they were still able to robustly optimize the models and apply data augmentation techniques to guarantee consistent system performance across different operational scenarios. Focused on bridging the accessibility gap for precision farming technologies and fostering data-driven practices in agriculture, AgroVision addresses gaps related to sustained and inclusive agricultural advancement.

Date: 2025
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