Titlebook: IoT and AI in Agriculture; Smart Automation Sys Tofael Ahamed Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive li

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Bernstein-test

Optimization of Soil-Based Irrigation Scheduling Through the Integration of Machine Learning, Remotand opportunities associated with these technologies. In particular, it underscores the potential benefits and the challenges involved in utilizing soil moisture sensors, remote sensing data, and ML techniques in synergy. The section emphasizes the importance of data quality assurance, interdiscipli

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nocturnal

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Eeg332

Strategic Short Note: Climate Smart Technology for Corn Production in Rizal, Kalinga, Philippines,ributed to climatic hazards such as typhoon, monsoon rains, drought, and prolonged dry season affecting the municipality. These phenomena put pressure on the production causing damages and reduction in the yield of corn. For farmers to adapt or mitigate the negative effects of these hazards, several

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SLING

Automatic Navigation of Pesticide Spraying Vehicle for Orchard Tree Trunk Detection,nducive to the operation of large-sized machinery. A small automatic spraying robot can alleviate the problem of labor shortage in orchard. The dense canopy of the orchard not only blocks the GNSS signals. However, the lack of light reduces the accuracy of object recognition by normal RGB camera. In

渐变

Driver Safety System for Agricultural Machinery Operations Using Deep Learning Algorithm, of the driver for subsequent safe handling. This research aims to identify and classify the driver’s actions while driving to determine whether the driver is in dangerous behavior and thus be able to raise an early warning. Considering that there are few studies and datasets in agricultural driving