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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM39774

SMART BOTANICA: AI-DRIVEN MULTI-SPECIES PLANT CARE ASSISTANCE AND MANAGEMENT SYSTEM

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM39774

Volume:

8 |

12 |

Dec 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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10.55041/IJSREM39774

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MTG

Mrs. Trupti Gangakhedkar

DKC

Dharshan Kumar C

NSB

Nithya Shridhara Bhat

S

Srajan

YHS

Yashavantha H S

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Abstract

Smart Botanica represents an innovative system that harnesses artificial intelligence and the Internet of Things to transform the management of plant care. By merging cutting-edge technologies with intuitive user interfaces, this project utilizes a blend of IoT sensors, AI algorithms, and real-time data analytics to track essential factors such as soil moisture, nutrient content, and overall plant health. The system features AI-driven image recognition capabilities to identify plant diseases and pest issues, offering precise diagnoses along with actionable treatment suggestions. Among the notable features of Smart Botanica are automated irrigation, disease forecasting, resource optimization, and comprehensive health reporting. Its modular architecture allows for scalability, making it suitable for both small home gardens and extensive agricultural operations. The user-friendly interface promotes interaction, enabling users to monitor plant conditions, obtain care recommendations, and create performance reports. Furthermore, the system includes an AI-powered query-handling module that enhances user engagement and facilitates knowledge exchange. By integrating automation, accuracy, and sustainability, Smart Botanica effectively tackles contemporary challenges in plant care, including resource inefficiency, slow disease identification, and the knowledge gap among users. This document outlines the system’s architecture, methodology, and testing outcomes, illustrating its capacity to revolutionize plant management practices and foster sustainable agricultural methods.

KEYWORDS

Python Tkinter-GUI Arduino MEGA 2560 Open CV Scikit-learn PySerail MySQL DHT 22 NPK Sensor IR Sensor Moisture Sensor.

Cite This Article

Mrs. Trupti Gangakhedkar, Dharshan Kumar C, Nithya Shridhara Bhat, Srajan, Yashavantha H S, "SMART BOTANICA: AI-DRIVEN MULTI-SPECIES PLANT CARE ASSISTANCE AND MANAGEMENT SYSTEM," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 12, Dec 2024. https://doi.org/10.55041/IJSREM39774

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Publication Date

12/16/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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