Agricultural Greenhouse Research Articles

Traditional and Novel Organophosphate Esters in Plastic Greenhouse: Occurrence, Multimedia Migration, and Exposure Risk via Vegetable Consumption.

The migration and risk of organophosphate esters (OPEs) in agricultural air-soil-plant multimedia systems due to plastic film application remain unclear. This study investigates the multimedia distribution of traditional OPEs (TOPEs), novel OPEs (NOPEs), and their transformation products (POPEs) in plastic and solar greenhouses. The total concentration of OPE-associated contaminants in air and airborne particles ranged from 594 to 1560 pg/m3 and 443 to 15600 ng/g, respectively. Significant correlations between air OPE concentrations and those in polyolefin film (P < 0.01) indicate plastic film as the primary source. Contaminants were also found in soils (96.8-9630 ng/g) and vegetables (197-7540 ng/g). The primary migration pathway for NOPEs was particle dry deposition onto the soil and leaf, followed by plant accumulation. Leaf absorption was the main uptake pathway for TOPEs and POPEs, influenced by vegetable specific leaf surface area. Moreover, total exposure to OPE-associated contaminants via vegetable intake was assessed at 2250 ng/kg bw/day for adults and 2900 ng/kg bw/day for children, with an acceptable hazard index. However, a high ecological risk was identified for NOPE compounds (median risk quotient, 975). This study provides the first evidence of the multimedia distribution and potential threat posed by OPE-associated contaminants in agricultural greenhouses.

Exploring the role of high-value crops to reduce agricultural greenhouse gas emissions in New Zealand

This study explores the potential benefits of transitioning from livestock farming to high-value alternative crops as a strategy for mitigating agricultural greenhouse gas (GHG) emissions in New Zealand. The government has set ambitious targets for reducing methane (CH4) emissions. However, since animal product exports play a crucial role in New Zealand’s economy, any shift in land use will have significant impacts on both the regional and national economy. We developed a GIS framework that integrated (i) growing requirements, (ii) GHG emissions and (iii) profitability for crops. Analysis of export market opportunities identified twelve high-value “alternative crops”. Availability of suitable land for crop expansion was not a limiting factor. Working with the Ministry for Primary Industries, we explored how land use change scenarios contributed to Government 2050 biogenic CH4 emission reduction targets. Doubling the area of alternative crops (a 195,000-ha increase) by reallocating land from livestock farming resulted in reducing biogenic CH4 emissions by 1.2 to 5.4% (0.35 to 1.57 Mt CO2-e) compared to 2017 baseline values, contributing to between 2.6 and 22.5% of the 2050 CH4 reduction targets, simultaneously increasing profitability by $NZ1.25 to 1.32 billion annually. While this approach demonstrates potential benefits of land use change, a deeper understanding of the complexity of land use decision-making is required to enable successful transitions. Addressing barriers to change requires collaborative efforts from land users, researchers and policy makers.

Climate change and income inequality in Sub-Saharan Africa (SSA): effects and transmission channels

Purpose The purpose of this paper is to assess the direct effect of climate change on income inequality in Sub-Saharan Africa (SSA) and the channels through which it spreads. Design/methodology/approach Using a sample of 38 countries, the authors specify and estimate a panel data model using the generalized least squares method over the period 1991–2020. Robustness is achieved through the generalized moment method-system. Findings The results show that an increase in vulnerability to climate change is positively and significantly associated with an increase in income inequality. The results also show that the effects of climate change are mediated by gross domestic product/capita, population and agriculture at the 15%, 17% and 24% thresholds, respectively. Research limitations/implications The authors suggest the implementation of inclusive development policies consistent with climate mitigation and adaptation objectives; the creation of financial spaces from various sources to finance the social security of the most vulnerable; and the strengthening of agricultural resilience to climate-related adverse events, including financing for greenhouse agriculture. Originality/value On the positive side, it contributes to the literature on the analysis of the direct and indirect effects (transmission channels) of climate change on income inequality in SSA. Methodologically, the study goes beyond previous work as it adopts a stepwise methodology, dealing with the endogeneity issue. At the logical level, it offers some non-exhaustive suggestions of potentially interesting economic policies to guide policymakers in their common commitment to “reduce income inequality” (Sustainable Development Goal 10, target 10.1).

Design of wireless transmission system for intelligent agricultural greenhouse data acquisition based on modbus communication protocol

Abstract Traditional agricultural greenhouses rely too much on manual labor and have a poor ability to adjust the key factors that affect crop growth, such as temperature, humidity, and illumination, resulting in low production efficiency and unstable product quality. Based on the MODBUS communication protocol, a set of intelligent agricultural greenhouse data acquisition wireless transmission systems is designed in this paper. Intelligent instruments, sensors, wireless transmission equipment, and smart control equipment (PLC) have been introduced in agricultural greenhouses. These sensors and instruments automatically collect data and wirelessly transmit it to the intelligent control system in the control room, which is located a few kilometers away. The intelligent control system processes the data and generates various control instructions which are then transmitted to the executive mechanism in agricultural greenhouses for implementation. We complete automatic adjustment of temperature, humidity, illuminance, and other parameters and operations. The intelligent, large-scale, and unmanned agricultural greenhouses are realized. It greatly improves the production efficiency and product quality of crops.

Enhancing efficiency in closed agricultural greenhouses: A data-driven predictive model for energy consumption

Abstract Predicting energy consumption in agricultural greenhouses is essential to effectively allocate resources, enhance plant growth, and minimize energy inefficiencies. Various factors affect the energy consumption inside the greenhouse including external climate conditions and internal microclimate. Proper understanding of these factors is crucial for maintaining an ideal growing environment and optimizing energy efficiency. This drives the need to investigate the interaction between these factors and greenhouse energy consumption, encompassing the energy needed for cooling and the supply of water and nutrients. This work aims at developing a dynamic model that predicts the total energy consumption of a closed agricultural greenhouse to improve microclimate control and energy efficiency. The study is conducted within a closed-loop agricultural greenhouse with no natural ventilation. Inside, the air is cooled and continuously circulated without being exchanged with ambient air through a heating, ventilation, and air conditioning (HVAC) system. The data-driven model encompasses external climate parameters such solar radiation, ambient temperature, and relative humidity; along with microclimate parameters such as internal temperature, humidity, and CO2 concentration to predict overall energy consumption. The study examines two machine learning models, deep neural networks (DNN) and extreme gradient boosting (XGBoost), for forecasting energy consumption, and assesses their performance using the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). Results reveal that the DNN model surpasses the XGBoost model, exhibiting a superior predictive performance with an R2 of 80.9%, RMSE of 171.1 kWh and MAE of 130.3 kWh. This study demonstrates its practicality in assisting with energy consumption analyses and identifying inefficient energy usage patterns within closed agricultural greenhouses.

Research on the Coupling Effect of Water and Fertilizer on Maize under Multi-Objective Conditions and Its Application Scenarios

It is of great significance to establish maize water and fertilizer application schemes under multi-objective conditions to improve water- and fertilizer-use efficiency, reduce agricultural greenhouse gas emissions, and promote sustainable agricultural developments. This study aims to analyze the effects of different water and fertilizer combinations on the summer maize yield, water-use efficiency, and field N2O flux and to determine the optimal water and fertilizer application scheme for summer maize. Field experiments were conducted in 2023, with a total of 15 different combinations of upper and lower limits of irrigation and fertilizer levels. A binary quadratic regression model based on the yield, water-use efficiency, and N2O emission flux was constructed. The fast non-dominated sorting genetic algorithm III (NSGA-III) was employed for verification and solution finding to simulate the optimal water and fertilizer regime. The results indicate that with increasing water and fertilizer applications, the field N2O emission flux gradually increases. The summer maize yield and water-use efficiency show a trend of initially increasing and then decreasing. Compared to fertilization, irrigation has a more significant impact on the summer maize yield and water-use efficiency, while fertilization notably influences the field N2O emission flux to a greater extent. Using NSGA-III, the simulated optimal water and fertilizer combination showed no significant difference in the yield and water-use efficiency compared to the actual optimal water–fertilizer irrigation combination (moderate water and moderate fertilizer), with a 3.12% increase in the field N2O emission flux, a 15.30% decrease in the irrigation amount, and an 11.90% reduction in the fertilizer application. In conclusion, employing the optimized water and fertilizer combination can reduce agricultural irrigation and fertilization while ensuring crop yields, providing theoretical support for the green, efficient, and sustainable development of the summer maize industry.

Перспективы переработки угля с низкой степенью метаморфизма

Introduction. The relevance and significance of this work is due to the fact that the soils of Kazakhstan are about 48% salt marshes. The reasons for their occurrence are different: harsh climatic conditions, the drying Aral Sea, the Baikonur cosmodrome, numerous test sites, etc. With an increased content of water-soluble salts in the root layer of soils, most cultivated plants develop poorly and yield a reduced yield. To solve the above problem, the technology of processing brown coal from the Kiyakty deposit and its substandard part for the production of drugs-growth stimulants for agricultural plants, high-calorie fuel briquettes and coal-alkali reagents is proposed. Materials and methods. Development of technology for the production of a humic preparation from brown coal deposits in Kazakhstan. It is known that these coals are amorphous polymers containing bitumen, humic acids, residual organic substances and moisture. Laboratory experiments were carried out by the method of biotesting according to B.P. Strogonov in glasses with a capacity of 0.5 liters in accordance with the requirements of GOST (GOST 10250–80, GOST 12038–84). The methodology for conducting research on the manufacture of briquettes, carbon-alkali reagents based on the humic acid hypothesis and various local binding materials with the involvement of certified CeLSIM laboratories is substantiated. The most optimal binder is a liquid bardic concentrate. Results. As a result of the application of new methods of pre-sowing treatment of rice seeds with a 2.5 % aqueous solution of the preparation from coal, its yield increased by 9.5–12.1 quintals per 1 ha, or by 33.8–41 % compared with the control. Discussion. The works of a number of foreign and domestic researchers on this topic have proved the possibility of using humic substances from brown coals to obtain organo-mineral fertilizers, and a comparative study of the chemical composition has been conducted. However, according to our technology, it is clear that we do not get fertilizer, but plant growth stimulants. It should be noted that the technology developed by the authors makes it possible to obtain a humic preparation, the use of which on low-yielding soils of Kazakhstan ensures a stable increase in the yield of cereals and legumes. Conclusion. The developed technology for the production of humic preparations for various purposes can be used to increase crop yields on degraded soils, for the bio recultivation of man-made formations, the manufacture of a substrate for greenhouse farms, the production of fuel briquettes and coal-alkali reagents, as well as during the operation of other brown coal deposits of the Republic of Kazakhstan with a high content of humic acids in their composition. Suggestions for practical applications and directions for future research. The territory of Kazakhstan is considered to be arid zones of dry steppe soils without rains. In these conditions, agricultural technicians resort to plants of halophyte varieties, therefore, the prospects for using this technology are very large, for example, to suppress dust formation on the surface of tailings ponds with the cultivation of green mass of wild plant varieties. Preparations from brown coals can be used to purify water and wines, leach precious metals, etc.

Microbial community dynamics and nitrogen transformation associated with turbid-turned culture stage of Pacific white shrimp, Litopenaeus vannamei in small greenhouse farms

Small greenhouse farms have experienced rapid expansion of Litopenaeus vannamei culture in China in recent years. To investigate microbial community dynamics and nitrogen transformation during the early critical stage, known as the turbid-turned stage in small greenhouse farms, microbial samples from the water column of small greenhouses were analyzed by 16S rRNA and 18S rRNA high-throughput sequencing, along with the detection of ammonia nitrogen in water. The results of water quality analysis revealed that accumulated ammonia nitrogen was converted to nitrite as the water color transitioned from clear green to turbid brown. High-throughput sequencing data demonstrated a shift in dominant microorganisms from zooplankton to algae and bacteria during the turbid-turned stage, and the main functional microbial taxa, such Marivita and Chlorella, were enriched. The results of Linear Discriminant Analysis Effect Size and canonical correlation analyses indicated that the compositions of key microbial communities were correlated with environmental factors. Furthermore, analysis of functional genes indicated that ammonia-oxidizing bacteria were present in the early growth stage, while the abundance of nitrite-oxidizing bacteria was low during the early culture stage. Overall, the findings suggest that microorganisms during the turbid-turned stage in small greenhouses may play a crucial role in regulating water quality and promoting shrimp health within a stable small greenhouse environment.

Enhancing predictive accuracy of inside temperature and humidity in an agricultural greenhouse using data-driven modeling with artificial neural networks

Enhancing predictive accuracy of inside temperature and humidity in an agricultural greenhouse using data-driven modeling with artificial neural networks

Լոլիկի ողկյուղավոր հիբրիդների աճեցման արդյունավետությունը ջերմատան պայմաններում / The Efficiency of Growing Raceme Tomato Hybrids Under Greenhouse Conditions

The scientific research was conducted within 2021-2023 in a glass-enclosed experimental greenhouse of the Scientific Center of Vegetable and Industrial Crops of the Ministry of Economy of the Republic of Armenia, located in the Darakert community of the Ararat region, and covered two growing cycles: spring and summer-autumn. The study focused on 5 raceme tomato hybrids: Prodezo F1 by Rijk Zwaan, Santiana F1, Adventure F1, Climbo F1 by Syngenta, and hybrid RM-4, which we bred (referred to as Syune since 2023). Prodezo F1 hybrid, which is included in the state register of Armenia, served as the control. The research aimed to analyze and assess the economic viability of cultivating both imported and local raceme tomato hybrids, identify the most promising varieties, and recommend them for production investment. Analysis of the research data indicated that the domestic RM-4 (Syune) hybrid and the foreign hybrids Adventure F1 and Climbo F1 demonstrated reduced production costs per unit and increased net income and profitability compared to the control hybrid Prodezo F1. Thus, the local RM-4 (Syune) hybrid and the foreign hybrids Adventure F1 and Climbo F1 displayed the highest profitability and quality indicators, making them recommended choices for investment in greenhouse farms.

Benchmarking techno-economic performance of greenhouses with different technology levels in a hot humid climate

Greenhouse agriculture is expected to play a critical role in sustainable crop production in the coming decades, opening new markets in climate zones that have been traditionally unproductive for agriculture. Extreme hot and humid conditions, prevalent in rapidly growing economies including the Arabian Peninsula, present unique design and operational challenges to effective greenhouse climate control. These challenges are often poorly understood by local operators and inadequately researched in the literature. This study addresses this knowledge gap by presenting, for the first time, a comprehensive set of benchmarks for water and energy usage, CO2 emissions (CO2e) contribution, and economic performance for low-, mid-, and high-tech greenhouse designs in such climates. Utilising a practical and adaptable model-based framework, the analysis reveals the high-tech design generated the best results for economic return, achieving a 4.9-year payback period with superior water efficiency compared to 5.8 years for low-tech and 7.0 years for mid-tech; however, the high-tech design used significantly more energy to operate its mechanical cooling system, corresponding with higher CO2e per unit area (8.3 and 4.0 times higher than the low- and mid-tech, respectively). These benchmarks provide new insights for greenhouse operators, researchers, and other stakeholders, facilitating the development of effective greenhouse design and operational strategies tailored to meet the challenges of hot and humid climates.

Usability assessment of a greenhouse context-aware alert system for small-scale farmers

In the dynamic landscape of modern agriculture, integrating technology holds immense potential to enhance efficiency and productivity for small-scale farmers. This study presents a user-centric evaluation of an intelligent context-aware alert system, tailored for small-scale greenhouse farming. We employed standardized questionnaires, including the NASA Task Load Index and the User Experience Questionnaire, to assess the system's perceived utility, mental workload, and overall user experience. Our findings reveal the high perceived utility of the system among farmers. Farmers participating in the assessment indicated a strong intention to utilize the system for crop monitoring. Moreover, the system demonstrated a moderate mental workload, suggesting ease of use and potential acceptance by users. Our evaluation highlighted an excellent user experience, with scores ranging from very good to extremely good across all dimensions. Furthermore, user preferences for alert mechanisms underscored the importance of adaptable notifications, with voice and text alerts favored for comprehensive information dissemination. Light and voice alerts were preferred during manual tasks. This study highlights the significance of user-centered design in agricultural technology, offering insights to enhance the usability and the adoption of alert systems in small-scale farming environments. The positive reception of the system's utility and the moderate mental workload suggest that such technology can be readily adopted by farmers, thereby improving monitoring and management practices in greenhouse farming. The preference for adaptable alert mechanisms further emphasizes the need for flexible and context-sensitive solutions in agricultural technology.

AGRICULTURE AND GREENHOUSE GAS EMISSION – RESULTS OF ECONOMETRIC ANALYSIS

Agriculture represents the “cause” and the “victim” of climate change. Almost 30 percent of greenhouse gas emissions come from the agricultural sector. They contribute to global warming and therefore significantly affect the sustainability of agricultural production systems. The aim of the paper is to determine which factors have the greatest influence on the greenhouse gas emission from agriculture. For this purpose, a dynamic panel analysis for 26 members of the European Union in the period from 2013 to 2021 in the paper is conducted. The results of the analysis suggest that the capacity for biofuel production, organic agricultural production and greenhouse gas emissions from the previous period have the greatest impact on the same emissions from agriculture. The results of this research can serve policy makers in formulating strategies for the development of food systems that will pollute the environment to a lesser extent and use available resources more rationally.

The Application of IoT Technology and Deep Learning in Automated Intelligent Control Systems

In order to accurately monitor the environmental information of agricultural greenhouses, achieve remote automatic control, and improve crop yield, the author proposes an application method of Internet of Things technology in automatic intelligent control systems. This method utilizes IoT technologies such as WSN, Android, and cloud platforms to design an intelligent agricultural greenhouse monitoring system, taking sensors such as soil moisture, lighting, temperature and humidity as examples, and using shading, water spraying, fans, and fill lights as control devices. Design from aspects such as system architecture, perception control system, cloud database, and mobile terminal management system to achieve automatic monitoring of agricultural greenhouse environmental information.Experimental results show that: The data collected by the sensor is compared with the actual monitoring data of the instrument, taking temperature as an example, the errors are all within ±0.5◦C of multiple measurements. Conclusion: The system has the advantages of good scalability, convenient networking, and high cost performance, which makes up for the difficulties in wiring and inconvenient use of the traditional agricultural greenhouse monitoring system, and has high practical value.

Integrating Agricultural Emissions into the European Union Emissions Trading System: Legal Design Considerations

In the European Union, greenhouse gas emissions statistics indicate only a slight decreasing trend over the last number of years in emissions from agricultural sources. Unless drastic action is taken in other sectors, the European Union’s 2030 and subsequent climate targets are unlikely to be met without greater reductions made in agricultural emissions. The policy instruments aimed at reducing agricultural emissions that are currently in place have proven to be ineffective; therefore, there is a need to look for new approaches towards bringing agricultural emissions down faster and farther. One obvious new approach is to integrate agricultural emissions into the European Union Emissions Trading System, which, so far, has proven very successful in reducing greenhouse gas emissions in the energy and industrial sectors. Hardly any attention has been paid in the scholarly legal literature to the question of integrating agricultural GHG emissions into emission trading systems. This article seeks to fill this gap. This paper presents the concluding findings of a Dutch Research Council-funded research project that aimed to assess whether and under what conditions the European Union Emissions Trading System could play a role in compelling the agricultural sector to reduce its greenhouse gas emissions. We answered this question by looking at lessons learned from existing examples in the world of market-based approaches to integrating agriculture into emission reduction schemes. To do this, we performed an ex-post assessment of three of the very few examples that exist in the world of such schemes in Canada, California, and Australia, followed by an ex-ante assessment of the prospect of including agricultural emissions under the European Union Emissions Trading System based on the practical experiences of those examples. In the ex-ante study, we evaluated how such inclusion could work, either indirectly, through allowing on-farm offset programs to reward increased carbon sequestration, or directly, by requiring farmers and/or other actors in the agricultural sector to surrender allowances for their direct emissions. As lawyers, we focused mainly on the legal considerations of such a proposition. Having conducted both the ex-ante and ex-post assessments, we conclude that introducing stricter legal instruments of one form or another that will reduce agricultural greenhouse gas emissions and increase carbon removal on agricultural land seems necessary for the European Union if it is serious about achieving its commitments under the Paris Agreement and meeting its obligations under its own Climate Law. The project makes a novel contribution to the legal scholarship in concluding that the most viable starting point for such stricter legislation would be to include methane and nitrous oxide emissions from livestock keeping and synthetic fertilizer use, respectively, under the European Union Emissions Trading System. To start with, this could be conducted by obliging meat and dairy processors and synthetic fertilizer producers to surrender allowances for the on-farm emissions associated with their products. This could be complemented by introducing a voluntary, but still highly regulated, carbon credits scheme that could encourage and reward farmers for reducing their own emissions and for transitioning to net-zero, and overall, more climate-resilient and environmentally friendly farming practices. Such credits could be offered for sale on the private carbon market as well as to Member State governments and the European Commission (through, for example, the Common Agricultural Policy, State Aid schemes, or the Innovation Fund).

Integrating semantic segmentation and edge detection for agricultural greenhouse extraction

Integrating semantic segmentation and edge detection for agricultural greenhouse extraction

Enhancing sustainable and climate-resilient agriculture: Optimization of greenhouse energy consumption through microgrid systems utilizing advanced meta-heuristic algorithms

Greenhouses offer controlled microclimates that enable year-round cultivation, improving food security and agricultural productivity. However, greenhouses are energy-intensive, with heating accounting for a significant portion of the associated costs. This study explores optimal microgrid configurations, economic viability, and policy recommendations for sustainable greenhouse agriculture in Nigeria. An in-depth energy assessment of a reference greenhouse in a South Korean facility is conducted. Distinct climatic differences between South Korea and Nigeria are highlighted, emphasizing the need for tailored greenhouse designs and energy solutions. Shifting focus to Nigeria, this study investigates the feasibility of hybrid renewable energy systems with a focus on wind and solar power across six geopolitical zones in Nigeria. The analysis encompasses technical, economic, and policy aspects, providing a holistic perspective on renewable energy adoption. Notably, the study uses an advanced optimization model, Teaching and Learning–Based Optimization algorithm, to assess the net present cost and baseload supply reliability, offering valuable insights for investors and policymakers. The result indicates diverse energy requirements across Nigeria, with total monthly peak energy demands ranging from 5374.80 kWh in the Southeast to 17,115.76 kWh in the Northwest, and a notable variation in the Levelized Cost of Electricity (LCOE), with the lowest at $0.07327 in Kano. Specifically, in Ogun, the net present cost for the WT-PV-ESS system stood at $520,935.45, while the PV-ESS system cost was substantially lower at $500,444.41. This confirms the effectiveness of location-specific analysis and shows the suitability of photovoltaic–battery energy storage systems for Nigeria's diverse regions, with unique considerations for specific areas. Policy recommendations, including feed-in tariffs, renewable portfolio standards, net metering, research support, and market development, provide a holistic framework for the adoption of renewable energy and sustainable agriculture. Improving infrastructure, market access, and financing for smallholder farmers is integral for improving food security and standards of living in rural Nigeria. In conclusion, Nigeria can leverage renewable resources to revolutionize its energy and agriculture sectors, setting an example for a sustainable and resilient future.

Effectiveness of the Fuzzy Logic Control to Manage the Microclimate Inside a Smart Insulated Greenhouse

Agricultural greenhouses incorporate intricate systems to regulate the internal climate. Among the crucial climatic variables, indoor temperature and humidity take precedence in establishing an optimal environment for plant production and growth. The present research emphasizes the efficacy of employing intelligent control systems in the automation of the indoor climate for smart insulated greenhouses (SIGs), utilizing a fuzzy logic controller (FLC). This paper proposes the use of an FLC to reduce the energy consumption of a greenhouse. In the first step, a thermodynamic model is presented and experimentally validated based on thermal heat exchanges between the indoor and outdoor climatic variables. The outcomes show the effectiveness of the proposed model in controlling indoor air temperature and relative humidity with a low error percentage. Secondly, several fuzzy logic control models have been developed to regulate the indoor temperature and humidity for cold and hot periods. The results show the good performance of the proposed FLC model as highlighted by the statistical analysis. In fact, the root mean squared error (RMSE) is very small and equal to 0.69% for temperature and 0.23% for humidity, whereas the efficiency factor (EF) of the fuzzy logic control is equal to 99.35% for temperature control and 99.86% for humidity control.

IoT based Automated Greenhouse

The advent of the greenhouse as a sustainable form of smart agricultural farming present a best solution to address the challenges like food scarcity resulting from population growth, climate change and environment change. This method help in off-season crop cultivation even in severe climatic conditions within enclosed spaces. However, effective control and management of crop parameters within the greenhouse are essential. Leveraging advancements in the Internet of Things (IoT) introduces smart solutions for automating greenhouse farming parameters including plant monitoring, atmosphere control, and irrigation. The greenhouse is one of the growing technologies used for cultivation. This work proposes study of technologies used in the greenhouse for monitoring and control. This work analyses the different communication techniques used for monitoring the parameter of greenhouse using relays, sensors. With the help of Wi-Fi connectivity helps to analyse the real data over the cloud, real time working of the equipment. The advantage over the simple greenhouse so that the farmer can easily understand which technology suit the best. This also help farmer to adopt the best suited technique in greenhouse that are cost-friendly, easy to use, and have longer life. Keywords – IoT, Greenhouse, NodeMCU, Firebase.

Twindemic Threats of Weeds Coinfected with Tomato Yellow Leaf Curl Virus and Tomato Spotted Wilt Virus as Viral Reservoirs in Tomato Greenhouses.

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.