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Abstract-This work presents results for the path loss due to foliage at 2.4 GHz using RF equipment and XBee-Pro ZB S2B transceiver modules in Agricultural fields (Corn, Paddy and Groundnut) and Gardens (Coconut garden with green grass, open lawn with dry green grass and wet green grass) targeting short-range, near ground RF propagation measurements for planning and deployment of Wireless Sensor Communications for precise agriculture and plantation management applications. Path Loss (PL), Path Loss Exponent (PLE) and corresponding Root Mean Square Error (RMSE) values were deduced from the measured RSS from various positions in these environments. Empirical foliage loss prediction models such as COST 235, Early ITU Vegetation and Weissberger models were compared with the experimental results.

The growing interest in applications of wireless sensor networks (WSNs) in vegetation environment has made it important to understand and predict the impact of vegetation on coverage and signal quality. As most of the proposed vegetation attenuation models are mainly based on measurements in temperate climate their prediction accuracy needs to be tested in tropical climate. With the presence of high humidity and high temperature in the tropical climate the attenuation because of vegetation is bound to increase. Very few researches have been reported about the attenuation because of vegetation related to WSN applications in scrub forest and plantation environments from the regions having tropical climate in Indian subcontinent. This study focuses on short-range, near to ground received signal strength measurements at 915 and 2400 MHz to evaluate vegetation attenuation models for planning and deployment of wireless sensor communications/ networks in tropical climate for precision agriculture and plantation management applications. The measurements have been done in scrub forest, mango and guava plantation environment. It is found that International Telecommunication Union (ITU) Recommendation (ITU-R) and Weissberger models under predict vegetation because of attenuation, whereas the Cost 235 model has high prediction accuracy in the scrub forest and plantation environments in tropical climate.