CROPVISIONAI:ANAI-POWERED SMART FARMING ASSISTANT USING CNN AND MACHINE LEARNING

This report has presented an expanded description of Crop Vision AI, an integrated smart farming assistant that combines CNNbased crop disease detection, Random Forest yield prediction, Decision Tree fertilizer recommendation, and real-time weather retrieval within a single platform. Rather than treating these as separate tools, the proposed design routes farmer-submitted images and agronomic details through a coordinated pipeline so that disease, yield, fertilizer, and weather information can be reviewed together in one interaction. The approach is grounded in established techniques documented across the reviewed literature, and its principal contribution lies in the integration of these techniques into one farmer-facing workflow rather than in the introduction of an entirely new algorithm. While the system's practical usefulness remains dependent on the quality of its underlying datasets, the robustness of its trained models, the continued availability of external services such as the Weather API, and eventual validation under real field conditions, the work described here provides a structured foundation on which a more comprehensive AI-assisted farming platform can be developed. Continued dataset expansion, model refinement, and collaboration with agricultural experts are identified as the most direct paths toward realizing that broader goal.

[1]  S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, vol. 7, Art. no. 1419, 2016. [2]  D. P. Hughes and M. Salathé, "An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics," arXiv:1511.08060, 2015. [3]  K. P. Ferentinos, "Deep Learning Models for Plant Disease Detection and Diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018. [4]  E. C. Too, Y. Li, S. Njuki, and L. Yingchun, "A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification," Computers and Electronics in Agriculture, vol. 161, pp. 272–279, 2019. [5]  L. Breiman, "Random Forests," Machine Learning, vol. 45, pp. 5– 32, 2001. [6]  J. R. Quinlan, "Induction of Decision Trees," Machine Learning, vol. 1, pp. 81–106, 1986. [7]  A. Kamilaris and F. X. Prenafeta-Boldú, "Deep Learning in Agriculture: A Survey," Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018. [8]  S. Wolfert, L. Ge, C. Verdouw, and M.-J. Bogaardt, "Big Data in Smart Farming – A Review," Agricultural Systems, vol. 153, pp. 69–80, 2017. [9]  M. A. Khan et al., "Plant Disease Detection and Classification Using Deep Learning," IEEE Access, 2020. [10]            A. Kamilaris, "Deep Learning in Agriculture: A Survey," Computers and Electronics in Agriculture, 2018.