Silk is known as the queen of textiles due to its softness, durability, and luster. This textile is obtained from cocoons spun by larvae known as the silkworm. The combined effect of both temperature and humidity, determines the satisfactory growth of the silkworms and the production of good quality cocoons. For that rea- son, we propose a new prototype for silkworm incubators that monitors environmental conditions, created with Raspberry Pi due to its capabilities, features, and low cost. The prototype monitors the temperature, humidity, and luminosity in a silkworm incubator. The monitoring data are collected and saved on file hosting service, Google Drive, for subsequent analysis. Preliminary tests were gathered using the silkworm incubator of University of Cauca, Colombia.
The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.
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