Plant Growth Monitoring System Research Articles

Intelligent Plant Growth Monitoring System Based on LSTM Network

Intelligent Plant Growth Monitoring System Based on LSTM Network

Camera-based plant growth monitoring for automated plant cultivation with controlled environment agriculture

Building integrated photovoltaic thermal (BIPVT) greenhouse technology exhibits great potential to enhance crop yielding, energy harvesting, and water management for highly efficient farming and enables controlled environment agriculture (CEA) for automated plant cultivation. The proposed smart greenhouse system uses BIPVT, geothermal, image-based sensing, and control technologies for high energy efficiency and high-fidelity plant growth control. A camera-based, computer-controlled system was demonstrated to successfully monitor single and multiple food plants' growth. The growth rate over the life-cycle of the plant was automatically recorded by cameras without human interference. A novel algorithm was developed to extract the plants from the background of the image and then to measure the length from the ground to the top of the longest plant with different shapes or profiles. The growth rate was automatically generated as fundamental data for farming management and control. Because the plant growth is slow and the errors caused by image noise can be comparable with the actual growth when the interval between images is not large enough, the noise of a camera is evaluated by comparing multiple images under the same exposure condition, which is used to provide the error range of the measurements. The plant growth monitoring system will be integrated into an ongoing greenhouse project for smart greenhouse operation.

Design of IoT-Based Tomato Plant Growth Monitoring System in The Yard

The development of tomato plants to produce good fruit cannot be separated from environmental factors that affect their growth and development of tomato plants. These factors include soil moisture, soil pH, temperature, or the amount of light received by tomato plants. The need for water in tomato plants is also very important for their continued growth. Monitoring the development of tomato plants in home gardens based on IoT (Internet of Things) is a monitoring system that utilizes IoT technology to collect, transmit, and analyze data about tomato plants in real-time. In this system, sensors connected to the internet network will be installed on tomato plants to measure several parameters such as soil moisture, air temperature, light intensity, and soil nutrient / pH levels in plants. The collected data will be sent to an IoT platform that will be able to analyze the data. The results of the analysis will be used to make decisions regarding plant care, such as providing water or nutrients that the plants need to grow properly. With cameras to monitor the physical development of the plants, plant height, and fruit development. With this system, communities and farmers can grow tomato plants and can monitor and control plant conditions in real-time through smartphone applications. By utilizing IoT technology, monitoring the development of tomato plants becomes more efficient and accurate. Communities and farmers can take preventive measures to avoid plant disorders and diseases before it's too late, to increase the production and quality of crops.

Comparative analysis of IoT-based controlled environment and uncontrolled environment plant growth monitoring system for hydroponic indoor vertical farm

Agriculture is a very important economic sector that contributes to a nation's overall economic development. According to the UN Food and Agriculture Organization, with a rising population, the cereals demand will reach 3 billion tons by 2050. Also, the mission of Sustainable Development Goals (SDGs) is to provide zero hunger and sustainable agriculture by 2030. With a simultaneous decline in cultivable land and water scarcity, food production has to increase in order to achieve the above mission. Vertical farming is a current state of art agriculture technology to increase crop yield per unit area. This work focuses on designing and constructing an IoT-enabled smart vertical farming system with a controlled environment for plant growth. This system uses the hydroponic Deep Flow Technique (DFT), various sensors, and an auto pH and Total Dissolved Solids (TDS) balancing system. This paper provides a comparative analysis of IoT-based controlled environment vertical farming setup with the uncontrolled setup for Romaine lettuce in terms of plant growth parameters like plant height, maximum leaf length, maximum leaf width, and fresh and dry weight of the plant. The observed fresh weight of the aerial part for automated and unautomated setup is found to be 58.66 g and 48.81 g respectively. Also, the chemical analysis showed the plants possess the required optimum range of micro and macronutrients for both setups. The macronutrient results obtained for the controlled/automated (A setup), and uncontrolled/unautomated setup (U setup for Phosphorus (P), and Potassium (K) are (PA, PU) (5.91 g/Kg, 6.06 g/Kg), and (KA, KU) (67.03 g/Kg, 74.01 g/Kg) respectively.

An intelligent plant growth monitoring system based on ESP32 and IoT resource explorer platform

An intelligent plant growth monitoring system based on ESP32 and IoT resource explorer platform

Digital image processing methods for estimating leaf area of cucumber plants

Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.

Monitoring Mung Bean’s Growth using Arduino

Abstract In Indonesian, Horticulture is one part of agriculture that is very important for the survival of the Indonesian people. Crops are useful to meet people’s food needs. The cultivation of agriculture crop very dependent on climate, weather and external factors. The optimal environment will provide optimal plant growth results, so people must really pay attention to the condition of these crops. To maintain plant growth so that crops can grow well, it is necessary to monitor the crops. To find out how plant growth, which needed a system to monitor plant growth. This paper will discuss about the implementation of a plant growth monitoring system. This system will check the climate, weather, and external factors like air, soil, light, and others, which will affect the crops. Internet of Things (IoT) assumes a significant job in a large portion of the fields. The utilization of IoT expanded in view of the different preferences we can get from that. The horticulture is where a great deal of progress is required because that is one of the fundamental needs and a huge area of individuals is associated with that. All this information is sent by Arduino Uno dev boards to the Ubidots IoT (Internet of Things) cloud platform. Data in Ubidots will be sent to the user’s smartphone.

Yield Evaluation of Brassica rapa, Lactuca sativa, and Brassica integrifolia Using Image Processing in an IoT-Based Aquaponics with Temperature-Controlled Greenhouse

The paper introduced the development of a self-sustainable smart aquaponics system in a temperature-controlled greenhouse with a monitoring and automatic correction system using an Android device through the Internet of Things (IoT) and plant growth monitoring system through image processing using Raspberry Pi. The system involves the acquiring of real-time data detected by the light intensity sensor, and air temperature and humidity sensor. It also includes the monitoring of the pH level and temperature of the recirculating water of the system. If the acquired data is not within the threshold range, the correcting devices, namely grow lights, exhaust and inlet fans, evaporative cooler, aerator, and peristaltic buffer device were automatically triggered by the system to correct and achieve its normal status. The internet remote access includes the effective wireless transmission and reception of data reports between the system and an Android unit with the Android application in real-time. The study focused on the evaluation of two experimental set-ups comparing the plant growth between conventional soil-based farming and the smart aquaponics system using image processing. After data gathering, results showed that the smart aquaponics set-up successfully produced a yield better than the conventional farming set-up.

Non-destructive and cost-effective 3D plant growth monitoring system in outdoor conditions

There is a high demand for non-destructive systems in plant phenotyping which should precisely calculate plant growth and structure parameters. In this study, the growth of chilli plants (Capsicum annum L) was monitored in outdoor conditions. We also proposed a cost-effective and non-destructive solution for reconstruction of plants in 3D using a single mobile phone camera based on a structure from motion algorithm. Various plant growth and structure parameters such as number of leaves, plant height, and leaf area were measured from the reconstructed 3D models at different plant growth periods. The accuracy of our proposed system is measured by comparing the values derived from the 3D plant model with manual measurements. The results demonstrate that the proposed system has potential to monitor plant growth precisely and non-destructively in outdoor conditions when compared with state-of-the-art systems.

Automated Roof Top Plant Growth Monitoring System in Urban Areas

Automated Roof Top Plant Growth Monitoring System in Urban Areas

Implementation of IoT with Image processing in plant growth monitoring system

An automated system for monitoring the growth of plant can be done with appropriate taxonomies. Such kind of information can be useful for formers, botanists, industrialists, food engineers and physicians. This work combines Image Processing and IoT to monitor the plant and to collect the environmental factors such as humidity and temperature. In image processing, a recognition system capable of identifying plants by using the images of their leaves has been developed and with the help of the images use of pesticides can be controlled. The system runs pre-processing and feature extraction techniques on the image before a pattern matcher compares the information from this image with the ones in the database in order to get potential matches. The different features that are extracted and compared are the color, texture and shape of the leaf. Based on the pattern recognition a leaf can be identified as healthy or deceased. Here we combine IoT and Image processing. In addition, our system is simple to use, fast and highly scalable.

An Efficient Modern Irrigation and Plant Growth Monitoring System using Sensor Network

The Automated irrigation and plant growth monitoring system is used to optimize the use of water resource for Agriculture. The system consists of the distributed wireless sensor network of soil, moisture, temperature, color sensors. Moisture and temperature sensors are placed in the root zone of the crops. The controller unit is used to control the irrigation motor thereby controlling the water flow to the field. The microcontroller of the controller unit is programmed with threshold values of the temperature and moisture content. Color sensor is placed height of the plant leaf, according to the leaf color pesticides are automatically irrigated to the plants. High performance microcontroller PIC is used in the controller node. The Sensor nodes are power by the 9V battery which can be recharged with solar cell. GSM module is used to send the information about the field. Due to its least cost and high performance, the system can be used even in areas of limited water supply and plant growth monitoring.

植物工場における野菜の生育監視システムの開発

In the intentional production of crops in a plant factory, it is necessary to measure the progress of growth of an individual plant. There is a significant positive correlation between several growth indices obtained by image processing and the top fresh weight of lettuce plants (Lactuca sativa L. non-heading type) grown in a plant factory. The horizontal projected leaf area for several days after sowing made it possible to estimate the top fresh weight. Therefore, we have developed an automatic plant growth monitoring system for vegetables grown in a plant factory. This system is organized into five parts, CCD camera with a transfer mechanism, a personal computer for image processing CPU, a image memory unit, a TV monitor and a printer. To evaluate the growth of transplants individually, we have developed growth prediction function with fuzzy inference.Using this function, the growth of lettuce plants grown in a plant factory was estimated continuously and automatically by use of a low-cost automatic plant growth monitoring system with a personal computer.

植物工場における野菜の画像処理手法の開発

The image processing of plant growth is an effective tool for intact and non-destructive measurement, so as to be able to digitize continuously the images of plant or leaves with the computer system. However, it requires a lot of know-how responded to the features of the object plants. We have, therefore, developed several image processing methods for obtaining the accurate information about the vegetables grown in a plant factory. The image processing procedure of vegetables was composed of basical image processing functions such as sharpening, smoothing, expansion, contraction, deleting of noise and filling hole. In order to recognize individual plant growth continuously, we have developed the labelling algorithm by calculating the distance between center of gravity and set point. Furthermore, the transformation of growth was intelligibly observed by the superposition of outlines and the skeleton by thinning of binary images in the growth stage.In conclusion, these techniques made it possible to realize automatic measurement and development of the plant growth monitoring system in a plant factory.

蛍光ランプを用いた人工光型植物工場の研究 （その２） 画像計測による野菜の成育監視技術の開発

These studies were carried out for the development of non-invasive and continuous moni-toring technique of plant growth in the plant factories.In this kind of facilities, the environmental factors such as ambient temperature, relative humidity, light intensity, pH of nutrient solution are measured and reported with high accuracy. But, data on plant growth are not measured almost or at all even nowadays.We have, therefore, tried to apply the method of image processing and obtained sufficient results. They are summarized as follows.(1) We have developed the plant growth monitoring system by image processing with a personal computer for the plant factories.(2) The leaf area was regarded as the most useful index of vegetable (lettuce) growth as a non-invasive method.(3) It was linear to the fresh weight with high coefficient of correlation (>0.95) .(4) The scheduled harvest was achieved by these techniques.