In August 2020, we commissioned our first smart farming project on a 5-acre farm in Melaka, managed by an experienced farmer who had been working the land for years. The farm was divided into blocks, each taking turns to be watered and fertilised through a centralised fertigation system with four outputs, each controlled by a solenoid valve. We supplied the fertigation system and the IoT layer — soil moisture sensors, cloud monitoring, the works. The irrigation system itself, meaning the pipes, the pump, the emitters, was designed and built by the farmer, as it typically is.
Up until that point, our involvement in farming projects had been limited to the fertigation system. We would supply the Dosatron injector, the mixing tank, the controls, and the farmer would handle everything else. But this was during the smart farming wave, and we were excited about the possibilities of IoT — the idea that you could monitor soil conditions remotely, automate irrigation decisions, and take some of the guesswork out of farming. So we ventured into it.
What we discovered was that the soil moisture data we were collecting didn’t tell a consistent story. Different parts of the farm were behaving differently, and the readings weren’t adding up in the way we expected them to. It took some time before we understood why: the irrigation system that the farmer had put together — which was not our domain and not something we had evaluated before installing our system on top of it — wasn’t distributing water uniformly across the farm. Some zones were receiving more water than others, and because of that, the soil moisture readings were not representative of what the farm actually needed. The sensors were working fine. They were accurately reporting what was happening. But what was happening was a consequence of uneven water distribution, not soil conditions that we could meaningfully respond to with automation.
That was the aha moment for us. We realised that the IoT layer we were so excited about was built on a foundation that hadn’t been properly engineered, and that no amount of data or automation could compensate for that. Since then, we started offering turnkey irrigation system design and build — not just the fertigation and controls, but the full hydraulic system from the water source to the emitter, designed properly before anything else goes in.
I think about this a lot in the context of what has happened to Malaysian agriculture since Covid. A wave of professionals from other industries — engineers, doctors, people from oil and gas, manufacturing, finance — have entered farming, which is genuinely a good thing because they bring knowledge and discipline from their respective fields. But one pattern I’ve noticed is that many of them see IoT as a kind of insurance policy. The thinking, whether stated or not, seems to be that with enough sensors and automation, you can make up for limited farming knowledge, that the technology will catch what you miss and correct what you get wrong. And I understand the appeal of that idea, but it’s not how it works.
A farmer with solid fundamentals and no IoT will almost always outperform a farmer with poor fundamentals and an expensive monitoring system, because the monitoring system will faithfully report the consequences of the poor fundamentals without being able to do anything about them. Good execution with basic knowledge is better than bad execution with advanced tools. What IoT can do — and does do well, when the system underneath it is properly engineered — is give you visibility and control over a process that is already working. It makes a good system better. It doesn’t make a broken system work.
If you’re planning a smart farming or fertigation project and want to talk through whether your irrigation system is ready for automation, feel free to reach out to us at info@autoflotechnology.com.