Abstract
When agricultural automation systems are required to send cultivation field images to the cloud for field monitoring, pay-as-you-go mobile communication leads to high operation costs. To minimize cost, one can exploit a characteristic of cultivation field images wherein the landscape does not change considerably besides the appearance of the plants. Therefore, this paper presents a method that transmits only the difference data between the past and current images to minimize the amount of transmitted data. This method is easy to implement because the difference data are generated using an existing video encoder. Further, the difference data are generated based on an image at a specific time instead of the images at adjacent times, and thus the subsequent images can be reproduced even if the previous difference data are lost because of unstable mobile communication. A prototype of the proposed method was implemented with a MPEG-4 Visual video encoder. The amount of transmitted and received data on the medium access control layer was decreased to approximately 1/4 of that when using the secure copy protocol. The transmission time for one image was 5.6 s; thus, the proposed method achieved a reasonable processing time and a reduction of transmitted data.
Highlights
Internet of Things (IoT) and artificial intelligence (AI) have gained considerable research attention in the field of agricultural work automation
The bean sprouts were placed near a window with a thin curtain, and the environment was simulated wherein sunlight besides direct sunlight gradually changes the brightness in the image
The extracted target image was saved as a Joint Photographic Experts Group (JPEG) file with quality 100
Summary
Internet of Things (IoT) and artificial intelligence (AI) have gained considerable research attention in the field of agricultural work automation. Methods for counting fruits [1] and flowers [2] using field images have been investigated to predict the yield of crops. Methods used to predict the required amount of irrigation based on cultivation field images have been studied [3]. The more complicated the cultivation environment recognition is when using AI, the higher the computing power required. When an AI-based system runs on a cloud, devices on the cultivation field can be used to transmit the image files to the cloud; most cultivation fields tend to lack communication infrastructure. These devices transmit the image files using a mobile communication network. When the shooting time is 12 h/day and the image size is 1920 × 1080, the amount of image data per month is approximately 30 GB
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
