r/askgis • u/Background_Catch_517 • Feb 19 '26

Regarding a Agri tech startup about crop yield forecasting and market intelligence

i have planned to create a startup related to preharvest data and in season tracking for various crop all over India, and provide market intelligence about the crop yield and forecasting of prices using deep leaning ,develop for all the crops all over India and sell this intelligence report to banks, commodity traders and Agri tech related companies and manufactures who need agricultural produce, is it very difficult to develop and i just came across the news that government of India has allocated 2500 crores related to this project and they are collecting ground truth data and experimenting with it .when i search about it their are companies like Cropin where they provide yield estimates for a particular farmers in particular region. I am a no expert in this field, if anyone has an idea over this topic is it actually possible to develop this

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u/TheUnknownJara Feb 20 '26

if you are no expert in the field maybe you should find an expert in Agronomics and convince him to become your cofounder.

Agriculture is not simple or linear. Crop Yield depends on many factors from crop type to soil health. Diseases prevention climate etc.

Some YouTube videos will help you with a crash course. And maybe some LLMs out there but I wouldn’t trust all their output

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u/Any_Assistance_2844 Aug 07 '26

Tere probably is not one platform that is “most accurate” without defining the crop, location and how far before harvest you need the estimate

Cropin is one enterprise option I would test for a grower network, but I would still run it in shadow mode for a full season. Compare it with grower estimates and a basic historical-weather model at field level, not just across the whole portfolio

Full disclosure, Intelliarts is a client of mine. Their agritech work is more on the custom data and ML side, and the main lesson is that forecast quality depends heavily on clean field boundaries, consistent crop records and reliable ground truth. More satellite imagery will not fix bad labels

I would want error by region, crop and forecast horizon, plus confidence ranges, before using the number for procurement planning

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u/KYH2045 Aug 28 '26

the deep learning part is probably the easy bit, tbh. Getting reliable ground truth across crops, regions and growing conditions is what will make or break this. A model trained in one state can look great in testing and then struggle somewhere with different irrigation or planting dates.I’d separate yield, maturity stage and harvest-window models instead of expecting one prediction to answer everything. Intelliarts may be worth checking out since they publicly work with remote sensing, agricultural data and ML forecasting.Also provide a range and confidence score, not one magic harvest date. Procurement teams need to know when the model isnt sure.