By Mengisti Berihu Girmay
Agriculture, like many industries, is continuously evolving through technological innovations. One example is precision agriculture—a practice that employs data collection and analysis to optimize the use of inputs such as water, fertilizers, and pesticides based on local environmental conditions at the sub-field level. Artificial Intelligence (AI) and the availability of low-cost sensors have renewed interest in precision agriculture and have broadened areas of application to the livestock sector. Falling costs have further increased the accessibility of these tools to smallholder farmers in low- and middle-income countries (LMICs).
In the livestock sector, sensors attached to animals or installed in barns can monitor physiological and environmental parameters in real time. Combined with AI-based pattern recognition, these tools can detect animal health issues (e.g., lameness) using image data or respiratory diseases (e.g., coughing) through sound analysis. This not only improves animal welfare but also enables early interventions, reducing the need for antibiotics and helping prevent larger disease outbreaks.
Yet, despite their promise, AI tools can raise serious concerns for farmers—particularly when the reasoning behind predictions is unclear. Fears of error, surveillance, or loss of control can undermine trust. If tools are seen as opaque or externally imposed, they may simply go unused. Making AI intuitive and transparent and aligning it with farmers’ realities is essential to ensure it supports, rather than complicates, decision-making in livestock systems.
Why explainability matters
Unlike the rule-based systems of conventional computer programming, in which the decision-making logic is predefined and transparent, many AI models operate as “black boxes”—making it difficult or impossible for users and often even developers to understand exactly how certain conclusions are reached.
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