Automatic Field Monitoring and Detection of Plant Diseases Using IoT
Keywords:
Automatic Field Monitoring, Detection of Plant Diseases, Internet of Things
Abstract
This research presents a GSM-based system for automatic plant disease diagnosis and describes its use in the creation of ACPS. Traditional farming methods were largely ineffective against microbial diseases. In addition, farmers can't keep up with the ever-changing nature of infections, so a reliable disease forecasting system is essential. To circumvent this, we employ a Convolutional Neural Network (CNN) model that has been trained to examine the crop image recorded by a health maintenance system. The solar sensor node is in charge of taking pictures, sensing continuously, and automating smartly. An agricultural robot is sometimes known as an agribot or agbot. An autonomous robot with agricultural applications. It helps the farmer improve crop productivity while decreasing the need for manual labour. In the future, these agricultural robots could replace human labour in a variety of farming tasks, including tilling, planting, and harvesting. These agricultural robots will manage pests and diseases as well as perform tasks like weeding. In order to keep an eye on the crops and streamline the irrigation process, this system is equipped with disease prediction technology for plants and intelligent irrigation controls. The energy required to provide disease prediction and irrigation systems separately is reduced by combining them in this project.
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Published
2023-08-11
How to Cite
Akila Begum H, Yasmin A, & T. Ashmi. (2023). Automatic Field Monitoring and Detection of Plant Diseases Using IoT. International Journal on Orange Technologies, 5(8), 5-24. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/4691
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