An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating.

Abstract

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO 2 ) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO 2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Plant phenotyping relevance

植物状態を監視するAI・IoTシステムを設計し、画像処理とノイズ除去によって葉上の害虫を検出する手法を中心的に開発・試験しているため、植物状態の計測プラットフォームとして収録する。

Code and data availability

The article states that a sample of the live video processing and the code are available in the Supplemental Files, which would qualify as paper-specific assets. However, no public URL is provided in the supplied blocks or the allowed URL list, so no actionable public asset can be verified.

No evidence-backed public reproduction asset is currently recorded.