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SMART POT: AN INTELLIGENT PLANT CARE ASSISTANT
Abstract
The SmartPot project is an integrated, IoT-based tomato plant care system that simplifies maintenance and im- proves the care of tomato plants by monitoring environmental conditions, detecting disease, and offering remedies. SmartPot uses an ESP32 microcontroller to collect real-time data from sensors measuring soil moisture, temperature, humidity, and light intensity, with this data stored in Firebase and accessible through a user-friendly Flutter-based mobile app. This makes it possible for users to monitor the health of their plant remotely and get alerts when conditions need adjustment. A standout feature of SmartPot is its disease detection model, powered by a YOLO model running on a Raspberry Pi. This model analyzes images to detect common tomato plant diseases, allowing early intervention. Once a disease is detected, SmartPot offers tailored remedies through the app, suggesting care ad justments or treatments. Additionally, a voice-activated assistant powered by a Large Language Model (LLM) that gives users the ability to ask questions without having to do anything, and it helps them with their plants using a speaker. Through IoT, machine learning, and natural language processing, SmartPot makes tomato plant care more accessible, responsive, and efficient to gardeners and tomato plant enthusiasts.
References
K. Rawal and G. Gabrani, IoT based computing to monitor indoor plants by using smart pot, in Pro- ceedings of the International Conference on Innova- tive Computing & Communications (ICICC), 2020. doi: 10.2139/ssrn.3562964.
Roja D, N. Venkatesh, S. Maheswari P. Triveni, and Ch. Srikanth, Harnessing IoT for Real-Time Plant Health Monitoring: Challenges and Opportunities, International
Journal for Modern Trends in Science and Technol- ogy, vol. 10, no. 02, pp. 61–69, Mar. 2024. doi: 10.46501/ijmtst1002009.
R. K. Jain, Experimental performance of smart IoT- enabled drip irrigation system using and controlled through web-based applications, Smart Agricultural Technology, vol. 4, p. 100215, Aug. 2023. doi: 10.1016/j.atech.2023.100215.
M. L. Gleason and B. A. Edmunds, Tomato Diseases and Disorders, Department of Plant Pathology, Iowa State University, Revised August 2006. [Online]. Available: https://www.extension.iastate.edu/store/ListItems.aspx? Keyword=pm230.
K. D. M, D. M. S, V. K. V, M. Jaincy D. E, K. R. A,
and S. Kumar R. M, FARMER’S ASSISTANT using AI Voice Bot, 2021 3rd International Conference on Signal Processing and Communication (ICPSC), IEEE, pp. 527– 531, May 2021. doi: 10.1109/icspc51351.2021.9451760.
DistilBERT: A distilled version of BERT. (2024, September 22). https://zilliz.com/learn/distilbert- distilled-version-of-bert
M. Umar, S. Altaf, S. Ahmad, H. Mahmoud, A. S. N. Mohamed, and R. Ayub, “Precision agriculture through deep learning: Tomato plant multiple diseases recognition with cnn and improved yolov7,” IEEE Access, vol. 12,
pp. 49167–49183, 2024.
Koroteev, M. V. (2021). BERT: A Review of Applications in Natural Language Processing and Understanding (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2103.11943
Kluepfel, M. (2024, November 21). Tomato Diseases & Disorders — Home & Garden Information Center. Home & Garden Information Center. https://hgic.clemson.edu/factsheet/tomato-diseases- disorders/
Tomato: Diseases and symptoms. (n.d.). Agriculture. https://agriculture.vikaspedia.in/viewcontent/agriculture/crop- production/integrated-pest-managment/ipm-for- vegetables/ipm-strategies-for-tomato/tomato-diseases-
and-symptoms?lgn=en
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