Posts

Plenty’s AI Post-Mortem: What Went Wrong in the Vertical Farm?

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  In 2021, I published " Cultivating Confusion: How AI Research is Misguidedly Transforming Agricultural Innovation ," a critical analysis that astutely observed a burgeoning trend: computer scientists and engineers, often lacking profound agricultural understanding, were increasingly driving AI research in the sector. My analysis voiced deep concern about the over-reliance on technology, the neglect of fundamental agricultural principles, and the unrealistic expectations surrounding AI's potential. Now, roughly four years later, the recent bankruptcies of companies like Plenty, as highlighted in my subsequent analysis " The CEA Mirage: When Tech Hubris Meets Agricultural Reality ," stand as a stark and almost prophetic validation of those very concerns. Building upon that more recent piece, this article dissects Plenty's failure through the lens of my earlier analysis, revealing the unlearned lessons and the perilous trajectory of a technology-first agricul...

Garbage In, Garbage Out: How Sensor Accuracy Impacts AI-Driven Precision Ag

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  Accurate sensor data is the backbone of effective AI-driven precision agriculture. The 'garbage in, garbage out' (GIGO) principle underscores that poor data quality leads to poor model performance, impacting all precision ag modeling efforts. We'll illustrate this concept using precision irrigation as a key example. Precision irrigation aims to optimize water use by precisely matching irrigation to crop needs, where soil moisture sensors play a crucial role in providing data that informs irrigation decisions. But what happens when the data you're feeding into your system is inaccurate? That's where the concept of 'garbage in, garbage out' (GIGO) becomes particularly relevant. Let's illustrate this using a simplified example: linear regression. Linear Regression: A Basic Tool for Prediction (and Why Garbage-Out is Inevitable) Linear regression is a straightforward statistical method used to model the relationship between two variables. In our case, we ...