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@article{180468,
        author = {Mrs. Chandana H M and Sharanabasappa and Y Tejas and Shreyas S and Venugopal Gowda},
        title = {Crop Stress Detection Using Ai Image Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1820-1824},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180468},
        abstract = {Alright, let’s ditch the lab coat and talk like 
real people for a second. Crops deal with all sorts of 
drama—droughts, pesky bugs, random diseases, and, of 
course, everyone’s favorite: not enough nutrients. When 
this stuff hits, it kinda wrecks the whole food supply 
chain. Old-school ways of spotting stressed-out plants? 
Honestly, they’re a slog. Tons of manual labor, slow as 
heck, and you’re just begging for someone to mess upSo, 
here’s the cool part: this research is all about bringing in 
the big guns—AI and some slick image processing. 
Instead of squinting at leaves with a magnifying glass, 
we’re talking about training machines (think: those deep 
learning models, CNNs and all that jazz) to check out 
aerial and ground pics for signs of trouble. It’s like giving 
your fields a health checkup from the sky.And yeah, 
there’s a bunch of techy stuff under the hood—prepping 
the images, slicing them up, pulling out the good bits, and 
analyzing all sorts of crop features most people wouldn’t 
even notice. The models get fed tons of labeled pics 
(because, you know, you gotta teach them what “sick 
plant” actually looks like). Turns out, they get pretty darn 
good at telling stressed plants apart from the healthy 
ones.Bottom line? This isn’t just a gadget for show-offs. 
The whole setup means farmers can spot problems way 
earlier, fix things faster, and not waste a ton of cash or 
time. Real-time monitoring, less guesswork, and way 
more food on the table. Hard to argue with that.},
        keywords = {},
        month = {June},
        }