Smart aquaculture and precision agriculture

Food production systems at sea and on land generate data in hard-to-reach places: offshore mussel lines, scattered fields and smallholder farms. This work brings AI to where the data is, using low-power edge devices, IoT pipelines and mobile applications.

Aquaculture

Edge AI for buoy detection. A comparative study of YOLO frameworks running on edge hardware to detect buoys and support mussel farming operations (Narang et al., 2024).
Remote growth monitoring. A precision aquaculture framework that combines IoT sensing, blockchain and cloud integration to monitor mussel growth remotely (Narang et al., 2024).

Agriculture

Data collection framework. An IoT and AI framework for precision agriculture that addresses data gaps and overlapping survey areas (Narang et al., 2024).
Crop disease diagnosis. A three-tier deep learning framework with a mobile application for diagnosing diseases across multiple crops, published in Discover Artificial Intelligence (Kochhar et al., 2026). I was an equal-contribution and corresponding author.

Examples of rice leaf diseases (leaf blast, rice hispa, sheath blight) from the multi-crop dataset. Figure from Kochhar et al. (2026), Discover Artificial Intelligence, CC BY 4.0.

Architecture of the plant disease prediction app: the farmer photographs the crop, CNN models analyse the image through a Python API, and the predicted disease is shown in the app. Figure from Kochhar et al. (2026), Discover Artificial Intelligence, CC BY 4.0.

I was the first and corresponding author of the three aquaculture and agriculture conference papers above.

Aquaculture

Agriculture

References

2026

2024