Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge

Abstract

The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the demand for the automation of strawberry cultivation. Predicting the harvest of strawberries is an important research topic, as strawberry production requires the most manpower for harvest. In addition, the growing environment has a great influence on strawberry production as hydroponic cultivation of strawberries is increasing. In this paper, we design and implement an integrated system that monitors strawberry hydroponic environmental data and determines when to harvest with the concept of IoT-Edge-AI-Cloud. The proposed monitoring system collects, stores and visualizes strawberry growing environment data. The proposed harvest decision system classifies the strawberry maturity level in images using a deep learning algorithm. The monitoring and analysis results are visualized in an integrated interface, which provides a variety of basic data for strawberry cultivation. Even if the strawberry cultivation area increases, the proposed system can be easily expanded and flexibly based on a virtualized container with the concept of IoT-Edge-AI-Cloud. The monitoring system was verified by monitoring a hydroponic strawberry environment for 4 months. In addition, the harvest decision system was verified using strawberry pictures acquired from Smart Berry Farm.

IOT Monitoring System for Ship Operation Management Based on YOLOv3 Algorithm

“…To measure the performance and reliability of the research, it was found that the result of the average loss of work after 400 training iterations was significantly unchanged and stable, following the average falling curve of the number of returns in the training process. e increasing number of iterations shows that the algorithm integrates very quickly into the study [22]. e actual return curve is used to measure a classifier's index.…”

“…us, when the output of one of the images after characterization is limited by the sigmoid function [0, 1] in a variety of wings [22], the value of the transverse entropy function is used to measure the difference between the estimated value and the actual value of the neural network to find out the difference in the similarity of the characteristics of the vessel and improve the experience true. For M-ship models, the total loss is…”

Shape classification technology of pollinated tomato flowers for robotic implementation

“…Furthermore, advanced technology is underway to use robots with AI to support cultivation to reduce labor costs. For example, pesticide application 15 and automated harvesting 16,17 .…”

Strawberry Cultivation Techniques

“…Over the course of four months, 1.316.848 actual environmental data points pertaining to 13 data kinds were monitored. Using 1575 strawberry photos from the Smart Berry Farm and a Google Images search, the harvest time was predicted with a high accuracy rate of 98.267% [76].…”