<![CDATA[Study This study integration Internet of Things (IoT) technology and learning machine For optimize irrigation of land agriculture scale small . Method qualitative involving humidity sensor installation soil and temperature air , data processing local on edge devices using learning models machines , as well as interview deep with farmers . The results show that the edge- based model ‑achieves accuracy prediction daily water requirements with RMSE 3.9 mm and lower average water ‑usage 28%. Real ‑time response without internet dependency , mobile friendly ‑interface , as well as cost low operational speed up adoption technology . The implication is that the system This capable increase water efficiency , pressing cost production , and support agriculture sustainable at the level micro .]]>
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