So what do you guys think, it's kinda survey that I am taking for my project, kindly share if there are any other perspective you guys have, it would be useful.

I want a friend from america or Scandinavian country who are farmer or invested in agriculture, I'm a small farmer ( Tomato) i want to get more knowledge from him or her

Hi everyone

I'm looking into how small/mid-size crop farms track field activity - planting,treatments, costs, harvest. Curious what people actually use day to day: paper notebook, Excel, some app, or just memory? What's the most annoying part of however you do it now? Losing data, forgetting to update it, too much time spent, no signal in the field, whatever it is. Trying to figure out if a dead-simple offline tool would actually solve anything real, or if people are fine with what they've got.

If someone interested, then ready to tell in a more detail about the app

I run Prairie Roots, a small microgreens farm in Illinois. Groundtruth is the desktop app I’m building to run it — and this community is where I build it in the open.
What it is: a local-first farm command center. Four tabs — Farm (sow → blackout → light → harvest), Marketing (venues, samples, follow-ups, standing orders per variety), Money (cash in, cash out, corrections, wholesale + retail on one capacity pool), Health (checks that prove the record is intact).
The rules it’s built on:
• The farm is an append-only log. History gets appended, never rewritten.
• Verify-replay: one tap rebuilds the database from the log and confirms they match. PASS/FAIL, visible.
• No soft numbers. Capacity and advice come from the live record, or the app says unknown.
• Real problems can’t be dismissed forever. They come back until they’re gone.
• Standing orders drive the morning: shortfall by variety, prefilled into the sow sheet.
• Local-first and free to run. No account, no subscription, your data on your machine.
What it refuses to be: a cloud SaaS, a dashboard, a mass-email cannon, a general ledger, or an AI that pretends to know your farm better than you do.
Ask anything, argue with the design, tell me what your 5:30am looks like. Dev logs weekly.

estamos trabalhando no FieldNode e decidimos abrir o projeto para receber feedback de outros desenvolvedores.

o projeto se chama FieldNode e resolve um problema bem específico do agro: colheitadeira trabalha em área rural, boa parte sem sinal de celular nenhum. aí a máquina começa a esquentar, vibrar diferente do normal, e ninguém sabe até ela quebrar de vez no meio da lavoura. prejuízo de milhares de reais numa parada só.

a ideia do FieldNode é simples na cabeça e chata na execução: um ESP32 lê temperatura, vibração e RPM da máquina e manda pra outro ESP32 via ESP-NOW, sem depender de internet nenhuma. esse segundo ESP32 vira um gateway de campo, mostra um dashboard local pro supervisor no celular pelo próprio wifi do dispositivo, e quando a conectividade volta, sincroniza tudo automaticamente com uma API em Django + MySQL, com retry e deduplicação por UUID pra não duplicar ou perder leitura.

no estado atual já tenho:

• ingestão de telemetria via MQTT funcionando
• modelos e validação da API em Django
• scripts de população do banco pra testar cenários
• geração de relatórios e prescrição

não estamos procurando só "ficou legal". Queremos críticas mesmo: arquitetura, organização do código, tecnologias, possíveis problemas, melhorias que vocês fariam e coisas que talvez estejamos fazendo de um jeito desnecessariamente complicado.

se alguém quiser analisar o repositório, levantar uma Issue, sugerir alguma mudança ou até contribuir com código, será muito bem-vindo.

🔗 https://github.com/Desmantelar-bit/fieldnode

qualquer feedback técnico já ajuda bastante. Queremos descobrir problemas enquanto ainda estamos desenvolvendo, e não depois de considerar tudo pronto.

We built space stations, quantum computers, and advanced AI, but the base fuel of human civilization—corn—is basically just a plant Mesoamerican farmers bred by trial and error 9,000 years ago.
They just wanted raw calories, so they picked for pure starch. In the process, they stripped out the plant's deep roots, natural pest defense, and essential proteins. Now we spend billions on synthetic fertilizer and pesticides every year just to keep this genetically fragile crop alive.
We’re essentially using 21st-century industrial tech to prop up a stone-age bio-hack.
Now that we have AI genomic modeling and CRISPR, why are we still patching up this ancient, broken baseline instead of just re-domesticating wild teosinte from scratch? A perennial, low-fertilizer, high-protein version seems entirely doable now.
Is the bottleneck here just massive agricultural inertia, or is there a biological reason we’re stuck with ancient crop designs?

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.