Smart Plant Research Articles

AI-Powered Companion Plant Care: A Jetson Nano-based Smart Plant Cultivation Device for Urban Homes

AI-Powered Companion Plant Care: A Jetson Nano-based Smart Plant Cultivation Device for Urban Homes

A novel integrated prediction method using adaptive mode decomposition, attention mechanism and deep learning for coking products prices

Accurate prediction of coking product prices is crucial for enhancing production efficiency, cost optimization, and profit maximization in smart coking facilities. To address the volatility caused by nonlinear factors such as raw material costs, substitutes, macroeconomic indicators, sudden events, policy changes, and market behaviors, we propose a novel integrated prediction method for coking product price prediction. This method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition, Bidirectional Encoder Representations from Transformers (BERT) for natural language processing, attention mechanisms (AT) to weigh feature importance, and an ensemble of Bidirectional Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, abbreviated BBG, for robust feature extraction. We design a feature selection strategy to avoid data leakage and improve the predictive ability of the model, and describe a method to maintain textual data information integrity when combining data from different sources. Experimental results on coke and methanol datasets show that our approach retains multi-source text richness improves predictive capability, and outperforms other state-of-the-art methods, providing an effective tool for developing smart coke plants.

Machine learning regression-based prediction model for the autonomous control of coagulant dosing in smart water purification plants

ABSTRACT The global issue of water scarcity is escalating due to urbanization and increased demand. This paper proposes a machine learning (ML) regression-based model for automatic coagulant dosing control in smart water purification plants (SWPPs).1 The model uses random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and k-nearest neighbors (KNN) algorithms. Performance metrics include MAE, MSE, RMSE, MAPE, and R2. The RF algorithm showed superior performance, with MAE of 0.005, MSE of 0.002, RMSE of 0.05, and MAPE of 0.000 for anion-poly aluminum chloride dosing, and MAE of 0.007, MSE of 0.00, RMSE of 0.02, and MAPE of 0.000 for Polymax dosing. The RF model's performance is due to its robust handling of large datasets and ensemble learning approach. Limitations include testing only two coagulants and reliance on historical data. These findings suggest integrating advanced water management, energy systems, and facility management to make SWPPs feasible and efficient, establishing a foundation for future applications of ML in chemical processes.

Strategies for Effective Product Roadmap Development and Execution in Data Analytics Platforms

Manufacturing is only one of several industries going through a digital transformation in this era of digital disruption. Manufacturing businesses are racing to adopt IoT-based solutions in order to innovate, increase productivity, lower costs, and gain greater market share because of the enormous transformational potential offered by Internet-of-Things (IoT) & Big Data. It is being used by industrial companies all over the world to increase flexibility while achieving cost savings, reduced inefficiencies, and better performance. It is no longer a trend for the future. But putting Industry 4.0 technology support into practice is a tough endeavour that gets increasingly harder in the absence of a common method. These are produced during the course of business operations and are kept in databases, email communication, transaction logs, free form texts on (business) social media, and other places. Businesses want to integrate data analytics methods into their decision-making processes by utilisation of these data. The field of Big Data analyses has seen tremendous progress in the IT sector in recent years. It appears that in order to deal with the extremely dynamic situations of today, new methods for product route mapping are required. This article provides an overview of the literature in science on product road mapping in order to shed light on the current state of the art & pinpoint research gaps. In order to demonstrate the influence of Industry 4.0 technological innovations on the manufacturing sector, this study provides an organised and content-focused evaluation of the literature. This paper offers recommendations for converting a legacy manufacturing facility into an Industry 4.0-compliant smart plant. Research gaps encompass topics including how to include objectives or results in product roadmaps, how to match a roadmap with the item's vision, and how to include activities related to product discovery in product roadmaps.

Evaluating the Smart Technology Level of Water Purification Plants

Key TakeawaysThe features of a smart water purification plant (SWPP) can include autonomous operation of treatment processes, energy management optimization, and facility preservation, using approaches that include artificial intelligence technology.Evaluation indicators were developed to measure the level of technology in K‐water's SWPPs.Practical demonstration at three K‐water SWPPs was used to validate the effectiveness of these indicators.

IoT Based Smart Plant Monitoring System

The agricultural sector is in transition thanks to the Internet of Things (IoT), which is also helping farmers overcome the many obstacles they confront. The problems that IoT can help with are plant and soil monitoring, livestock monitoring, and conservation monitoring. Innovative IoT applications improve agricultural productivity in terms of quality, quantity, sustainability and cost-effectiveness while meeting industry challenges. This project automates the watering process while monitoring soil factors such as temperature, electrical conductivity, and moisture content. A microcontroller is used to make decisions. When a user is acknowledged in the field whether there is a text message variation from the anticipated values. This research also includes plant pest detection in addition to soil metrics. This guarantees whole system health. In contrast, greenhouses are regulated environments where plants are grown. The primary goal of this project is to create an Arduino- based system that is easy to use, inexpensive, and can be used to continuously upload and alter environmental parameter values in order to maximize plant growth and yield.

IOT based Smart Plant Monitoring System and Control System

The world is now fully dependent on the internet and the Internet of Things (IoT), which is the inter-networking of physical devices embedded with electronics, sensors, software and network connectivity. IoT enables physical devices to exchange data with the manufacturer and achieve greater value and services. Agriculture is the backbone of many countries, but water scarcity is a major issue in this field. To overcome this problem, the irrigation process can be automated using IoT. By using sensors, the temperature, humidity, and light can be measured, and appropriate processing can be performed based on the outcome. This helps in reducing water wastage and improving accuracy, economic benefits, and efficiency while reducing human intervention. This paper highlights the importance of IoT in day-to-day life, its scope in the future, and its potential applications in farming methods. To demonstrate the use of IoT in agriculture, we present the Automatic Watering System, which monitors and maintains the moisture content in the soil using temperature, humidity, and moisture sensors. The Arduino UNO microcontroller is used as the control unit, regulating the appropriate amount of water to avoid over/under irrigation. Keywords – sensors, temperature, humidity, soil moisture, Arduino uno

Process and quality evaluation of different improved composts made with a smart laboratory pilot plant

This study monitored the process and investigated the quality of compost obtained from different biomasses. Five blends of agri-food waste were composted by a laboratory pilot plant named COMPOSTER, that is designed to optimize biodegradation, and produce compost efficiently. The COMPOSTER consists of two 35-liter nearly adiabatic, aerated bioreactors that simulate an industrial process involving the typical sequence of mesophilic-thermophilic-mesophilic phases. It continuously monitors and records temperature, internal pressure, and biomass weight, while controlling and quantifying oxygen consumption and carbon dioxide emissions resulting from aerobic biodegradation. All composts were characterized for their main chemical, physical, and molecular features, as well as their suppressiveness against Fusarium oxysporum f.sp. lycopersici (FOL), tested on tomato seedlings. Optimized biodegradation yielded 50–60 % mature compost with a cumulative oxygen consumption ranging from 282 to 456 gO2 per kg of dry matter, with peaks of 2.55 gO2 per kg of volatile solids per hour, and carbon dioxide emissions of 22–36 % of the initial carbon content, with peaks of 5.89 g CO2 per kg of volatile solids per hour. Blends containing more ligno-cellulosic ingredients showed higher yields and lower CO2 emissions. Most of the nitrogen present initially was retained in the final compost; indeed, all mixtures exhibited an apparent nitrogen concentration increase due to carbon loss. Composting determined deep modifications in the molecular structure of the organic matter. 13C CPMAS-NMR and off-line thermochemolysis GC-MS analyses highlighted decomposition degree of polysaccharides and peptidic moieties, selective preservation of aliphatic and aromatic recalcitrant compounds, and optimal ongoing humification. All composts were non-phytotoxic, except for that including pepper crop residues, and all resulted rich in macro- and micro-elements for plant nutrition and proved to be active in controlling FOL disease. Compost comprising 81.2 % tomato crop waste exhibited the best growth performance and pathogen control on tomato. Mature, non-phytotoxic, nutrient-rich, and suppressive composts represent promising by-products that can be successfully recycled in agriculture, including high-value applications, leading to lower use of fertilizers and pesticides.

IOT Based Smart Plant Monitoring System

Abstract: The IoT-based Smart Plant Monitoring System represents a transformative approach to modern agriculture by leveraging the power of the Internet of Things (IoT) to enhance the monitoring and management of plants in agricultural settings. This system integrates advanced sensor technologies, wireless communication, and data analytics to provide real-time insights into the health and environmental conditions of plants, thereby optimizing resource utilization and improving overall crop yield. The key components of the proposed system include a network of sensors strategically placed within the agricultural field to capture vital data related to soil moisture, temperature, humidity, light intensity, and nutrient levels. These sensors are connected through IoT-enabled devices, forming a cohesive network that continuously collects and transmits data to a centralized cloud-based platform. The cloud platform acts as a repository for the acquired data, where advanced analytics algorithms process the information in real-time

IOT- Based Smart Plant Protection and Pest Control by Using Raspberry Pi

In economies heavily dependent on agriculture, such as India, the farming sector plays a crucial role, yet it faces various challenges that hinder its profitability and, unfortunately, contribute to farmer suicides. Pest attacks stand out as a significant factor contributing to the agricultural woes, causing substantial harm to crops. This research proposes a solution leveraging Raspberry Pi technology, incorporating a mathematical model known as Beta regression analysis. The model utilizes farm humidity and temperature as inputs to predict environmental conditions conducive to pest formation and attacks. The resultant Beta regression factor serves as a risk indicator for environmental health. Based on this factor, the system forecasts the likelihood of pest occurrences. By offering advance predictions of pest activity, farmers can strategically apply the right amount of pesticides, effectively mitigating the impact of pests on their crops. This proactive approach allows farmers to manage potential damage before it occurs, fostering a more sustainable and profitable farming environment. The innovative system outlined in this paper aims to empower farmers with accurate pest control predictions, thus enhancing their ability to navigate and overcome challenges in the agricultural landscape.

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach

ABSTRACT Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.

OPTIMIZING PLANT HEALTH THROUGH IoT

Design and implement a smart plant watering system that uses IoT technology to monitor and regulate moisture levels in flower pots. The system should be able to automatically adjust watering frequency and amount based on real-time moisture measurements to ensure plant health and minimize water loss. In addition to our commitment to holistic plant care, an additional feature, NPK fertilizer, has been seamlessly integrated into the system. Containing essential nutrients nitrogen, potassium and phosphorus, this feature optimizes plant growth and health. This system automatically applies fertilizer according to a pre-set schedule or on-the-fly adjustments, creating the perfect solution for efficient and sustainable plant growth. The main goal is to create convenient and sustainable solutions. Meets the needs of both experienced and novice plant enthusiasts. Keywords: Internet of Things, NPK fertilizer, humidity sensor, WiFi module, buzzer, motor.

Increased Accumulation of Ginsenosides in Panax ginseng Sprouts Cultivated with Kelp Fermentates

Currently, new agri-tech has been developed and adapted for the cultivation of crops using smart farming technologies, e.g., plant factories and hydroponics. Kelp (Laminaria japonica), which has a high industrial value, was considered as an alternative to chemicals for its eco-friendly and sustainably wide use in crop cultivation. In this study, a fermented kelp (FK) was developed for use in hydroponics. The FK contained various free and protein-bound amino acid compositions produced by fermenting the kelp with Saccharomyces cerevisiae. Supplementing FK as an aeroponic medium when cultivating ginseng sprouts (GSs) elevated the total phenolic and flavonoid contents. Additionally, seven ginsenosides (Rg1, Re, Rb1, Rc, Rg2, Rb2, and Rd) in GSs cultivated with FK in a smart-farm system were identified and quantified by a high-performance liquid chromatography-evaporative light scattering detector/mass spectrometry analysis. Administering FK significantly increased the ginsenosides in the GSs compared to the control group, which was cultivated with tap water. These results indicate the FK administration contributed to the increased accumulation of ginsenosides in the GSs. Overall, this study suggests that FK, which contains abundant nutrients for plant growth, can be used as a novel nutrient solution to enhance the ginsenoside content in GSs during hydroponic cultivation.

LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data

Abstract The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions. To achieve these objectives, sensors, monitoring systems, and artificial intelligence (AI)-based models are increasingly being developed and utilised for decision support and advanced control. Key to the adoption of advanced data-driven control of WWTPs is real-time data validation and reconciliation (DVR), especially for sensor data. This research demonstrates and evaluates real-time AI-based data quality control methods, i.e. long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models. The DVR procedure is aimed at anomalies resulting from data acquisition issues and sensor faults. Anomaly detection precedes the reconciliation procedure using models that capture short-time dynamics (SD) and (relatively) long-time dynamics (LD). Real data from an operational WWTP are used to test the DVR procedure. To address the reconciliation of prolonged anomalies, the SD is aggregated with an LD model by exponential weighting. For reconciling single-point anomalies, both ARIMA and LSTM AEs showed high accuracy, while the accuracy of reconciliation regresses quickly with increasing forecasting horizons for prolonged anomalous events.

A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant

A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant

Smart plant propagation algorithm for the improvement of self-excited induction generator performance

Smart plant propagation algorithm for the improvement of self-excited induction generator performance

Smart Plant Disease Analysis and Management System Using Ai and Iot

Plant diseases are one of the major causes of crop yield reduction worldwide. Early detection and effective management of plant diseases will ensure a sustainable food supply and reduce famine. In this project, we propose a Plant Disease Management System (PDMS) that uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies to diagnose and manage plant diseases. The proposed system consists of two main components: a plant disease diagnosis module and a disease management module. The plant disease diagnosis module uses AI algorithms to analyze images of plant leaves and identify the type of disease affecting the plant. The AI model is trained on a large dataset of plant images using deep learning techniques, such as Convolution Neural Networks (CNNs) and Transfer Learning.

Nanobiotechnology to advance stress resilience in plants: Current opportunities and challenges.

A sustainable and resilient crop production system is essential to meet the global food demands. Traditional chemical-based farming practices have become ineffective due to increased population pressures and extreme climate variations. Recently, nanobiotechnology is considered to be a promising approach for sustainable crop production by improving the targeted nutrient delivery, pest management efficacy, genome editing efficiency, and smart plant sensor implications. This review provides deeper mechanistic insights into the potential applications of engineered nanomaterials for improved crop stress resilience and productivity. We also have discussed the technology readiness level of nano-based strategies to provide a clear picture of our current perspectives of the field. Current challenges and implications in the way of upscaling nanobiotechnology in the crop production are discussed along with the regulatory requirements to mitigate associated risks and facilitate public acceptability in order to develop research objectives that facilitate a sustainable nano-enabled Agri-tech revolution. Conclusively, this review not only highlights the importance of nano-enabled approaches in improving crop health, but also demonstrated their roles to counter global food security concerns.

Smart Plant Pot Robot with IoT Integration for Indoor Ornamental Plants Care

Smart Plant Pot Robot with IoT Integration for Indoor Ornamental Plants Care

Photoluminescence studies of Eu3+ doped bismuth silicate based phosphor for plant grow LEDs

Artificial red lights of good quality are essential for indoor plant growth as it has a wide spectrum effects on photosynthesis rate and photomorphogenic processes of organic plant growth cycle. In present work we report on Eu3+ doped Bi4Si3O12 (BSO) phosphor for spectroscopic implication in smart plant grow LEDs. Eu3+ activated series of Bi4Si3O12 phosphor have been prepared via traditional solid-state reaction method. Crystal structure and phase formation characterization is determined by using X-ray diffraction (XRD). Photoluminescence spectra of prepared phosphor shows broad excitation spectra ranging from 200 to 530 nm. The optimal Eu3+ doping concentration observed at 3 mol%. Emission spectra observed under the excitation of 270 nm shows broad emission band at 469 nm along with other characteristic peak at 500 nm-710 nm. Observed emission peaks at 622 nm, 656 nm and 702 nm were chosen opt for plant grow lights. The phytochrome PFR absorption spectra of plants matches well with the emission band of our Eu3+ doped BSO phosphor and can be considered as a viable choice for solid state lighting and plant grow light LEDs.