Machine Learning Applications in Agriculture: A Software Engineering Perspective

Abstract

The integration of machine learning (ML) technologies within the agricultural sector is catalyzing significant advancements in precision agriculture, crop management, pest detection, and resource optimization. This paper presents an in-depth analysis of ML applications in agriculture through the lens of software engineering methodologies, emphasizing principles such as modularity, iterative development, and explainable AI (XAI) frameworks. By examining leading platforms such as Farmonaut, Taranis, and SatYield, this work highlights the effectiveness of ML-driven systems in solving domain-specific challenges while ensuring reproducibility, transparency, and scalability in compliance with standards advocated by professional bodies like the ACM. Furthermore, we critically investigate persistent issues including dataset limitations, inherent algorithmic bias, and the environmental footprint of model training, underlining the imperative for disciplined software engineering to foster ethical, sustainable, and scalable deployment of ML in agriculture.