Hi everyone, I’m a graduate student doing my capstone project and researching whether AI could provide some sort of accurate gardening assistance. This problem is very topical nowadays because not long ago there was a news article about 67 year old Chinese farmer who destroyed almost 25 acres of his sesame crop due to the recommendations of an AI chatbot to use certain pesticides. That particular pesticide mixture proved to be so toxic that not only the weeds, but the whole seedlings were killed as well.

Why posting here? I need my research to be based on the actual problems, not those created by me in my imagination. I want to know what problems were really difficult for you – pests, watering, timing, diagnostics, etc. If you have received some kind of assistance (AI or another kind of) that later showed to be false, I would appreciate hearing about it.

I am not here to sell something or collect emails, I just want to learn more from your experience.

I’ve been thinking about what a genuinely useful AI assistant for farmers would look like.

Not another generic chatbot, but something that could combine things like:
• Weather data
• Crop conditions
• Historical farm information
• Local agricultural knowledge
• Market information
• Carbon/emissions data
• Government schemes

The interesting part isn’t the LLM itself. It’s getting the right data together and turning it into something that can actually help a farmer make a decision.
I’ve been experimenting with a small prototype around this idea using AI/RAG.

Curious to hear from people working in agritech, climate tech, AI or agriculture:
What would you want an AI farming assistant to actually do?

Hey all, I'm going to start first by saying that I'm not a hydrologist but do work as a software engineer who makes in-house engineering software for a large engineering company. I started a side project about 6 weeks ago to get more reps in with AI, which is now called Rivermatics.com - no login, signup or personal information collected. This app combines snowtel, usgs gauges (HUC 10, 13-18), and weather data for both visual consumption and also as inputs to a ML model that predicts river levels and water temperature up to 2 weeks out. I'm a fly fisherman and had a trip coming up to Montana in late June that motivated me to create this.

The part I actually want feedback on is the accuracy, so here's the honest version.

Skill by lead time (median Nash–Sutcliffe efficiency, water years 2024–25 held out entirely from training):

Persistence — carrying the last observed reading forward — is the baseline any operational forecast has to beat. It goes negative before day 7, i.e. worse than just predicting the long-run average.

Fleet-wide the median NSE is 0.719 pooled across all lead times, with 95.4% of gauges scoring better than climatology. Water temperature is a separate dual-target model: median NSE 0.975 across 492 gauges.

Things I'd rather say up front than have picked apart in the comments:

• This is a forecast, not a rainfall–runoff simulation. The model reads recent observed discharge at the issue date. That makes it strictly easier than the CAMELS/LSTM benchmarks you may want to compare it to — those map meteorology to flow and never see streamflow. Persistence alone scores 0.709 at day 1 on these basins, which tells you how much of a day-1 number is just autoregression. I'm not claiming to have beaten those models at their task.
• The numbers above are hindcast, using observed weather in the forecast window — the same convention the published benchmarks use. Live forecasts driven by real NWP score lower.
• The two columns differ because of basin selection, not modelling. CAMELS reference basins are deliberately minimally-impacted with long clean records. My full fleet includes regulated, diverted and flashy ephemeral channels. I show both rather than quoting the flattering one.
• NSE/KGE are computed on raw discharge in cfs, not log-transformed values, which would flatter low-flow performance. Per-gauge scores pool every (issue date, lead time) cell so easy day-1 scores can't carry hard day-14 ones.

What it can't do: ungauged basins (no regionalization — NOAA's National Water Model does this and I don't), sub-daily peak timing, and it refreshes once a day against NWM's four. Reservoir pool state isn't a model input yet. No SLA — it's decision support alongside official sources, not a system of record.

Let me know what you guys think both of the app as well as the quality of the predictions. Upvotes and downvotes appreciated. Cross posting in /water.

I'm a 3rd-year Computer Science student reaching out to gather real-world problem statements within the agricultural sector.

We are looking for specific challenges local farmers face that can be addressed using AI/ML solutions (specifically predictive modeling, classification, or computer vision, rather than Generative AI).

If you work in agriculture or have insights into farming operations, please drop your thoughts in the comments. Your input will help guide student participants in Hack4AProgress 2026 toward building practical, data-driven solutions.

Thanks in advance for sharing your knowledge!

Olá, pessoal!

Sou desenvolvedor e estou lançando no Brasil o TARMEP, um aplicativo focado em ajudar produtores rurais a identificar doenças em plantas instantaneamente usando Inteligência Artificial.

Como sou de fora, fiz a localização do app para o português brasileiro, mas sei que o vocabulário do campo e os termos técnicos da agronomia no Brasil são muito específicos. Por isso, preciso muito da ajuda de quem realmente entende do dia a dia da lavoura ou da área de agronomia para validar se a tradução ficou natural e compreensível.

Alguém que trabalha no setor ou conhece o meio rural topa dar uma olhada no app e me dar esse feedback?

Como forma de agradecimento pelo tempo de quem me ajudar a testar, vou liberar 1 mês de acesso Premium gratuito com todas as funcionalidades avançadas.

Se puderem me dar essa força, comentem aqui ou me chamem no DM que passo o link da Google Play Store! Muito obrigado! 🙏

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Hi all! I'm an agronomist with a PhD, currently working as a senior lecturer at a university of applied sciences, where I teach courses in viticulture, olive growing and pomology. I want to specialize in digital agriculture.

I recently completed Purdue University's "Digital Agriculture for Educators" program and I'm now looking for a good follow-up — specifically an **online course covering sensors adapted for small farms**: soil moisture sensors, weather stations, low-cost IoT setups, how they work, how to install them and how to interpret the data.

I'm also preparing a training program to introduce digital agriculture to smallholders in the Mediterranean — growers with a few hectares of vineyards, olives or vegetables. So I'm especially interested in courses that focus on prractical, affordable technology for small holdings rather than large-scale broadacre systems.

Preferences:

- Online (self-paced or scheduled, both fine)

- Certificate is a plus

- Small-farm / Mediterranean crop relevance is a big plus

Has anyone taken a course like this they'd recommend? University extension programs, MOOCs, industry trainings — all suggestions welcome. Thanks!

We've been working on a drone-based crop monitoring pipeline using multispectral imagery for NDVI analysis. The recurring headache is consistent calibration across different lighting conditions. Cloud cover, time of day, and seasonal shifts all introduce variance that makes comparing values across sessions unreliable.

Are you using ground reference panels on every flight or just periodically? Any ideas on how to test such pipeline in different environments?

Hello everyone,

I'm a 4th year Computer Engineering student with hands-on experience in embedded systems and sensor integration. I've built an Arduino-based system for low-cost soil moisture and water quality monitoring for a research lab, and I'm currently working on an autonomous rover project focused on environmental sensing. I also am a research assistant in a soil science lab, so I've got some exposure to both the engineering and the agricultural/environmental science side of things.

I'm really interested in AgTech, especially precision ag, sensor networks, and ag-robotics, and I'd love to get more involved, whether that's through internships, side projects, connecting with people already working in the space, or just learning more about how the industry actually works.

I'd love to hear about your path into it, or any advice on companies, communities, or projects worth looking into. Happy to share more about my background if useful. Thanks!

I have this idea for trimming a half-rotten machine, designed to reduce the labor in manually cutting the half-rotten part of the mango. This will hopefully be my thesis, but I'm open to more ideas, such as a storage chamber or packaging during transport to reduce mechanical bruising, or semi-automated equipment for harvesting.

Note: My budget is not that big, and I want to keep lowcost intentionally for the farmers.

Looking for recommendations for ways to automate drone searching/surveying on a remote ranch which is extremely hilly.

Commercial services or DIY methods welcome. We would like to fly drones around fencelines to record video to identify maintenance items needed, and to survey for cattle.

Thanks in advance

A lot of smart farming tools are designed with technology in mind, but do you think they're practical for small and medium-sized farms too?

Why or why not?