• Swetha R
• Syeda Simran Baseer
• Fariya Bano
• Abhilash

The main objective of this project is to develop a system for plant monitoring and watering using the Internet of Things and Computer Vision. With Raspberry Pi as Processor, and sensors for sensing environmental conditions, the system monitors different parameters like Temperature, Humidity and Soil Moisture. We will also conduct the image analysis by combining the deep learning for tomato fruit detection and image processing for color feature extraction to classify the fruit’s maturity into a binary result of its growth stage (Eg., ripe or not ripe). In this work, we will collect images and detect the fruit section on images where the deep learning model will be trained to detect these fruit regions, then segment them from its background that may consist of soil and leaves. Finally, the system will notify farmers using an IoT notification platform. The observational phase of crop growth is essential to determine the quality aspect of agricultural sciences. This system enables the farmer to accomplish well-timed interventions that warrant for optimal yields at the end of the season. With the necessary information available regarding the problems that farmers face, we can accurately target those specific areas, while also accomplishing the efficient use of resources.

Internet of Things (IoT), Bot Notification, Tomato Maturity, Deep Learning, Image Processing, Computer Vision (CV)

"Detection of Tomato Growth State and Surveillance System using Computer Vision and Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.588-594, June-2020, Available :http://www.jetir.org/papers/JETIR2006083.pdf

"Detection of Tomato Growth State and Surveillance System using Computer Vision and Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp588-594, June-2020, Available at : http://www.jetir.org/papers/JETIR2006083.pdf