Imlisanen Jamir
Twelve high-definition cameras now cover two farm sheds in Nagaland, running through the night with infrared capability switched on. Every movement a Mithun makes gets logged, whether it is lying down, standing, feeding, or mounting, and each animal carries a digital identity that the system tracks continuously across the footage.
Built on YOLOv8n paired with DeepSORT, the technology can identify mounting behaviour at better than ninety-five percent confidence in conditions dark enough that a human eye would struggle. Researchers from ICAR-NRC on Mithun, along with collaborators at NIT Nagaland, Nagaland University and CHRIST University, frame the work as a tool for animal health and reproductive management, given how much cultural and economic weight the Mithun carries across the region.
Set against the surrounding landscape, the achievement takes on a different shape. Head into Nagaland's more remote districts and the gaps in basic infrastructure become obvious quickly: hours-long power cuts, mobile signal that disappears well before a village comes into view, roads prone to landslides with nothing in place to warn anyone in time. A school compound or market junction with working CCTV remains rare outside the larger towns.
Two farm sheds, by comparison, now enjoy round the clock infrared coverage more thorough than what most of the surrounding human population lives with after dark. Read that way, the project stops looking like a story about engineering progress and starts looking like a small, unintended illustration of where resources land first in a place where they are already thin.
The reason has little to do with anyone choosing animals over people. A Mithun's mounting behaviour looks roughly the same across three thousand annotated images, which is precisely why a model can be trained to recognise it. A collapsing hillside during monsoon season does not repeat itself the same way twice; the danger changes with the slope, the year's rainfall, whatever happened to be driving over it at the time.
Nothing about it holds still long enough to become clean training data. Research funding and technical capability tend to gravitate toward problems shaped like the first kind, since they can be measured, benchmarked and published, while problems shaped like the second kind resist that treatment no matter how urgent they are.
By the end, the technical details fade and the picture is what stays. An animal walks through infrared light in a shed while the hills around it remain dark and unwatched. Whatever arrives first in a resource-scarce place, it seems, arrives for whoever was easiest to observe.
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