Page 860 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Further efforts will focus on optimizing AI models for faster performance on edge devices to
support real-time decision making. Potential areas for scale up include forming partnerships
for broader deployment and extending the system to support multi crop disease detection
for wider agricultural impact.

3 Use Case Requirements
• REQ-01: It is required to collect high resolution images of tomato leaves for accurate
disease detection and model training.
• REQ-02: It is required to deploy edge computing hardware such as Raspberry Pi to enable
real time image processing on-site.
• REQ-03: It is required to install IoT sensors to monitor environmental conditions,
specifically temperature and humidity, in tomato fields.
• REQ-04: It is required to develop a mobile application equipped with a camera interface
and alert system to provide real-time notifications to farmers.
• REQ-05: It is required to ensure stable power supply and uninterrupted internet
connectivity to support continuous data transmission and system updates.

4 Sequence Diagram

5 References

[1] Kaggle. n.d. Accessed June 19, 2025. https:// www .kaggle .com/ .
[2] Singh, Vikram, Nitin Sharma, and Sandeep Singh. 2020. “A Review of Imaging Techniques
for Plant Disease Detection.” Artificial Intelligence in Agriculture 4: 229–42. https:// doi
.org/ 10 .1016/ .aiia .2020 .10 .002.
j
[3] Subeesh, Anil, and Chaitanya R. Mehta. 2021. “Automation and Digitization of Agriculture
Using Artificial Intelligence and Internet of Things.” Artificial Intelligence in Agriculture 5:
j
278–91. https:// doi .org/ 10 .1016/ .aiia .2021 .11 .004.
[4] Thangaraj, R., S. Anandamurugan, P. Pandiyan, and V. K. Kaliappan. 2022. “Artificial
Intelligence in Tomato Leaf Disease Detection: A Comprehensive Review and Discussion.”

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