Agrovision: Smart Solutions for Modern Farming.
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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.
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References
G L, N. V and S. U, ”A Review on Prediction of Crop Yield using Machine Learning Techniques,” 2022 IEEE Region 10 Sympo- sium (TENSYMP), Mumbai, India, 2022, pp. 1-5, doi: 10.1109/TEN- SYMP54529.2022.9864482.
S. Mohammed, R. Malhotra, M. D. Shamout, B. Pithadiya, S. Patil and S. M. Sangve, ”High-Accuracy Crop Yield Estimation Through IoT and Remote Sensing,” 2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), Indore, India, 2023, pp. 225-230, doi: 10.1109/ICPSITIAGS59213.2023.10527721.
Bhatt and S. Varma, ”Recommendation System for Crops Integrating with Specific soil parameters by Machine Learning Techniques,” 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 2023, pp. 1-8, doi: 10.1109/SCEECS57921.2023.10063029.
D. Pujari, R. Yakkundimath and A. S. Byadgi, ”Identification and clas- sification of fungal disease affected on agriculture/horticulture crops us- ing image processing techniques,” 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India, 2014, pp. 1-4, doi: 10.1109/ICCIC.2014.7238283.
Thakur, Sonu and R. Kumar, ”Object Detection approach for Crop and Weed Identification based on Deep Learning,” 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 2024, pp. 1-6, doi:10.1109/SCEECS61402.2024.10482324.
Jin et al., ”An Improved Mask R-CNN Method for Weed Seg- mentation,” 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), Chengdu, China, 2022, pp. 1430-1435, doi: 10.1109/ICIEA54703.2022.10006300.
Sethia, H. K. S. Guragol, S. Sandhya, J. Shruthi and N. Rashmi, ”Automated Computer Vision based Weed Removal Bot,” 2020 IEEE International Conference on Electronics, Computing and Communica- tion Technologies (CONECCT), Bangalore, India, 2020, pp. 1-6, doi: 10.1109/CONECCT50063.2020.9198515.
Celikkan, M. Saberioon, M. Herold and N. Klein, ”Semantic Segmen- tation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification,” 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 582-592, doi: 10.1109/ICCVW60793.2023.00065.
Steininger, Daniel and Trondl, Andreas and Croonen, Gerardus and Simon, Julia and Widhalm, Verena. (2023). The CropAndWeed Dataset: a Multi-Modal Learning Approach for Efficient Crop and Weed Manip- ulation. 3718-3727. 10.1109/WACV56688.2023.00372.
Suma, T., Kumar, S. V., and Sandhya, B. R. (2022). Classification of soil and prediction of fertilizer for specific crop cultivation using machine learning technique. International Journal of Health Sciences, 6(S8), 5468–5473.
Tulaskar, V., Dhoble, R., Bawane, D., Virmuttu, S., Ambagade, K., and Dhutonde, A. (2023). Soil classification and crop suggestion using ML. International Research Journal of Modernization in Engineering, Technology and Science, 5(6), 940.
Gaikwad, S., Aiwale, A., Rekade, V., and Kalunge, V. (2022). Soil classification and crop suggestion using machine learning techniques. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 10(V), 4984.
Barvin, P. and Sampradeepraj, T.. (2023). Crop Recommendation Sys- tems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review. 97. 10.3390/ecsa-10- 16010.
Hao-Ran Qu and Wen-Hao Su. (2024). Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review. Agronomy. 14. 363. 10.3390/agronomy14020363.
Hu, Kun and Wang, Zhiyong and Coleman, Guy and Bender, Asher and Yao, Tingting and Zeng, Shan and Song, Dezhen and Schumann, Arnold and Walsh, Michael. (2023). Deep learning techniques for in-crop weed recognition in large-scale grain production systems: a review. Precision Agriculture. 25. 1-29. 10.1007/s11119-023-10073-1.
K. More, C. Asodekar, V. Ghonghade, S. Mandlik, and S. Palkar, ”Agrovision: Smart Solutions for Modern Farming,” Journal of Emerg- ing Technologies and Innovative Research (JETIR), vol. 12, no. 1, pp. d343–d348, Jan. 2025.

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