ABSTRACT In an era where sustainable agriculture is imperative, precision crop management emerges as a vital strategy to enhance yield while conserving resources. The agricultural sector, increasingly shaped by advancements in Artificial Intelligence (AI), is progressively adopting gas sensors—particularly metal‐oxide semiconductor (MOS) types—as critical tools for data‐driven monitoring and decision‐making. Despite their potential, widespread use of gas sensors in agriculture remains constrained by high initial costs, limited user training, complex data requirements, and uncertain returns. This study highlights the transformative role of cost‐effective MOS gas sensors in early disease detection, yield enhancement, and system efficiency. Readily available from global retailers such as AliExpress, Amazon, and eBay—some priced as low as 0. 99—these sensors offer promising accessibility. However, challenges persist in VOC quantification, power consumption, selectivity, durability, and signal stability. The study explores these limitations alongside proposed solutions and research directions. AI methodologies such as Support Vector Machines (SVM), Partial Least Squares Discriminant Analysis (PLS‐DA), and Artificial Neural Networks (ANNs) show potential to improve selectivity and reduce drift error, contingent on access to large, labeled datasets. As technological refinement progresses, MOS gas sensors are poised to play an expanding role in precision agriculture, aligning environmental management with data‐centric innovation.
Ahmad et al. (Sun,) studied this question.