Soil Humidity Research Articles

Evaluation of Rain Shelter Cultivation Mode Effects on Microecological Environment of Mountain Cultivated Ginseng Rhizosphere

Rainfall, particularly in continental climates with a monsoonal tendency, impacts the microbial niches during the growth of mountain cultivated ginseng. With shifts in the microbial community, diseases in ginseng cultivated and protected under rain shelter conditions may ultimately be altered. Such cultivation may influence microflora dynamics through variations in meteorological parameters; however, this is not yet clear. The present study found that rain shelter cultivation affected the distribution of fungal communities within mountain cultivated ginseng. This led to an improved community structure in the ginseng rhizosphere, characterized by the proliferation of antagonistic fungi and a reduction in pathogenic fungi. A correlation analysis of meteorological factors found that soil temperature and humidity were the primary meteorological factors affecting mountain cultivated ginseng. It is evident that rain shelter cultivation regulated the microecological environment of the mountain cultivated ginseng’s rhizosphere and resulted in positive outcomes. A disease investigation supported this finding. The incidence of ginseng root diseases, such as rust and root rot, was reduced by 5–6%. The incidence of ginseng leaf diseases, including gray mold and black spot, was reduced by 5–10%. This research provides evidence to address the dynamics of microbial ecology under rain shelter cultivation and its benefits for sustainable mountain cultivated ginseng management.

Factors that influence the migration of sorbed pesticides in polyethylene and biodegradable mesoplastics

Trifluralin, Chlorpyrifos, and Procymidone migration performance from polyethylene (PE) and biodegradable (Mater-Bi: M-B) mulching films was examined. Desorption of pesticides from PE and M-B was studied using soil-plastic microcosms, considering temperature, soil humidity, and mulching film type as experimental variables. Trifluralin and Chlorpyrifos desorption was higher for PE than for M-B under all experimental conditions. In both cases, as the temperature increased from 25º C to 40º C, pesticide migration also increased, whereas as the soil humidity raised from 30% to 60%, pesticide desorption decreased. In the case of Procymidone, migration from PE and M-B at 25º C was similar under both soil moisture conditions. Migration percentages were similar for both mulch films at 40ºC and 30% soil humidity. However, at higher soil moisture (60%), migration from M-B was greater than from PE. A linear relationship was observed between the percentage of migration and the vapor pressure of the pesticides. In all cases, migration increased with higher vapor pressure, indicating a possible migration mechanism in the vapor phase. Pesticide migration increased at high temperatures (40º C). The effect of soil humidity in reducing pesticide migration was more significant at lower levels (30%).In addition, the mesoplastic sorption of pesticides in soil columns was studied using PE and M-B films. While the recoveries for Trifluralin, Chlorpyrifos, and Procymidone in the PE films were 0.05% ± 0.01%, 0.13% ± 0.03%, and non-detectable, the recoveries for M-B were: 0.49% ± 0.07%, 0.31% ± 0.09%, and 0.17% ± 0.10%, respectively, indicating that M-B was a better adsorbent than PE in all cases. This behavior should be considered in combination with the lower migration percentages observed for this type of mulching film in the microcosm experiments. These results could indicate a potential carrier effect of pesticide on biomesoplastic in the environment.

Effects of temperature, relative humidity and soil organic carbon content on soil-air partitioning coefficients of volatile PFAS

Soil-air partitioning coefficient (KSA) values are often used to assess the environmental fate of organic contaminants in soil. Till now, sufficient KSA values have not yet been measured for many compounds of interest, including some emerging pollutants such as volatile PFAS. Moreover, the effects of environmental factors such as temperature, relative humidity and soil organic carbon content on KSA of volatile PFAS are also unclear. In this study, the KSA values of target volatile PFAS were measured under various temperature (20–40 °C), relative humidity (30–100 %) and soil organic carbon content (2.1 %–8.0 %) using a modified solid-phase fugacity meter. The results showed that higher temperatures, higher relative humidity and lower organic carbon content in soil may accelerate the diffusion of target volatile PFAS. Furthermore, the KSA measurements were used to derive a multiple linear regression model to depict the relationship between logKSA and temperature, relative humidity, soil organic carbon content and PFAS-specific logKOA. When compared with the predictions obtained from semi-empirical model, we argued that the multiple linear regression model is more robust and easier to implement for target volatile PFAS or other emerging volatile PFAS than the semi-empirical approach to help depict the diffusion process at target volatile PFAS contaminated sites.

Smart-Agri Advisor: Data-Driven Crop Recommendations for Enhanced Productivity and Sustainability

In today's farming, it's important to grow crops well and use methods that are good for the environment to meet the rising need for food worldwide. This paper introduces "Smart-Agri Advisor," a system that helps farmers choose the right crops by analysing real-time data about the environment, soil, and market trends. It looks at factors like Soil contents, temperature, humidity and rainfall, along with market demand, to help farmers make smart decisions. The system also uses IoT sensors to automate watering, manage resources better, and make farming more sustainable. This study looks at how well the Smart-Agri Advisor helps improve farming efficiency and matches farming methods with market needs. Keywords: Machine Learning, NumPy, scikit- Learn, TensorFlow, Keras, etc.

A Review of Biological Control One Decade After the Sorghum Aphid (Melanaphis sorghi) Outbreak.

Melanaphis sorghi is a pest that is native to Africa but is now distributed worldwide. In 2013, its destructive capacity was demonstrated when it devastated sorghum crops in the United States and Mexico, making it a new pest of economic importance in North America. At the time, the phytosanitary authorities of both countries recommended the use of pesticides to control the outbreak, and biological control products for the management of this pest were not known. In response to the outbreak of M. sorghi in North America, several field studies have been performed in the last decade on sorghum crops in the USA and Mexico. Works have focused on assessing resistant sorghum hybrids, pesticide use, and recruitment of associated aphid predators and entomopathogens for natural control of M. sorghi populations. The objective of this review is to compile the information that has been generated in the past decade about indigenous enemies affecting M. sorghi naturally in the field, as well as the search for biological control alternatives and evaluations of interactive effects of resistant sorghum hybrids, pesticides, and natural enemies. To date, different predators, parasitoids, fungi, and bacteria have been evaluated and in many cases found to affect M. sorghi populations in sorghum agroecosystems or laboratory bioassays, and the use of resistant sorghum varieties and pesticides did not have clear toxic effects on natural enemy populations. Many of the macroorganisms and microorganisms that have been evaluated as potential biological controls have shown potential as alternatives to synthetic pesticides for keeping M. sorghi population densities below economic damage thresholds and are compatible with integrated management of sorghum aphids. While most tests of these biological alternatives have shown that they have aphidicidal potential against sorghum aphids, it is crucial to take into account that their effectiveness in the field depends on a number of abiotic and biotic factors, including soil texture, temperature, humidity, and natural enemies.

Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks

Precision agriculture has emerged as a promising approach to enhance crop yield, reduce environmental impact, and optimize resource utilization through advanced sensing and automation technologies. This paper proposes an energy-efficient and location-aware framework for Internet of Things (IoT) and Wireless Sensor Networks (WSN)-based precision agriculture systems. The framework leverages low-power wireless communication protocols, adaptive sensor scheduling, and location-based clustering algorithms to minimize energy consumption and prolong the network lifetime. Key features include real-time monitoring of soil moisture, temperature, humidity, and crop health through geographically distributed sensors, with automated decision-making for irrigation, fertilization, and pest control. The proposed framework also integrates machine learning models for predictive analysis and anomaly detection, enabling early identification of potential issues that could adversely affect crop productivity. Simulation results demonstrate a significant reduction in energy consumption and communication overhead, while maintaining high accuracy in environmental parameter monitoring and resource allocation. This framework offers a scalable and robust solution for implementing sustainable precision agriculture practices, particularly in remote and resource-constrained areas

DIGITAL TOOLS FOR MONITORING AND MANAGEMENT IN AGRICULTURAL PRODUCTION

The digitalization of agricultural production has led to the widespread use of mobile technologies for monitoring and managing agricultural processes. Applications and devices such as drones and satellites allow you to receive real-time data on soil conditions, humidity, plant growth, and pest threats. This data helps farmers to make quick decisions on irrigation, fertilization, and crop forecasting. Modern mobile solutions such as AgroMonitor integrate with various data sources and provide accurate information, which increases the efficiency of resource management and reduces production risks. The introduction of these technologies not only helps to increase yields but also ensures the sustainable development of agricultural operations, minimizing the impact of external factors. Thus, digital tools are becoming an important element of the modernization of the agro-industrial sector, contributing to its competitiveness at the global level.

Annual emissions of N2O, NO, HONO, and NH3 from maize-wheat fields in the North China Plain

Fertilized agricultural soil is a significant source of gaseous nitrogen compounds (GNCs), including N2O, NO, HONO, and NH3. The North China Plain (NCP) is the “hot region” for the release of these GNCs due to intensive fertilization practices. However, existing research has primarily focused on N2O emissions from fertilized farmland in the NCP, lacking comprehensive observational studies on other GNCs. Therefore, a continuous cumulative sampling technique (open-top dynamic chamber system) was utilized in this study to simultaneously measure the exchange fluxes of N2O, NO, HONO, and NH3 over summer maize-winter wheat rotation fields in the NCP. Results showed that GNC emissions from the soil displayed distinct diurnal variations, with higher emissions during the day attributed to elevated soil temperature. However, N2O emissions remained consistent between day and night, potentially influenced not only by soil temperature but also by soil humidity. Annual cumulative emissions and emission factors (EFs) for four GNCs were determined, indicating that N2O, NO, and NH3 emissions during the maize season were 1.38–2.37 times higher than those during the wheat season, with 98 % of HONO emissions occurring in the maize season. Additionally, the study first presented the annual HONO EFs of 0.36 ± 0.03 % in fertilized farmland. Furthermore, a comparison revealed that the fluxes of N2O, NO, NH3, and HONO using the conventional single-point sampling method were 26.4 %, 13.9 %, and 8.10 % lower, and 7.86 % higher compared to the continuous cumulative sampling method recommended in this study. In general, this study provided precise measurements of GNC emissions from farmland, offering essential foundational data for modeling parameters and contributing to the formulation of regional air pollution prevention and control policies.

Enhancing Agroecology in Pepper (Piper nigrum L.) Cultivation with Centrosema pubescens Ground Cover: A Study from Central Bangka, Indonesia

<p>Pepper cultivation in Bangka Island primarily takes place on sandy land. Legume cover crops, such as <em>Centrosema pubescens</em>, which has been widely used as a ground cover, are anticipated to improve land quality by maintaining soil temperature and humidity, increasing soil organic carbon content, increasing soil porosity, and improving soil fertility. This research aims to analyze the agroecosystem of pepper plants, by comparing the use of the cover plant <em>C. pubescens</em> as a soil treatment and the absence of using these cover plants as a control. The research was conducted in farmers’ pepper gardens in Perlang Village, Central Bangka Regency, Bangka Belitung Province. The research employed a randomized block design, with <em>C. pubescens</em> and natural vegetation as treatments, each replicated 3 times. The variables measured were soil temperature and humidity; abundance of microorganisms; weed density, frequency, and dominance; chlorophyll a, chlorophyll b, and anthocyanin content of pepper leaves; photosynthesis rate; transpiration rate; stomatal conductance; soil chemical and physical properties. The results showed that <em>C. pubescens</em> as ground cover could reduce the dominance of the <em>Bidens pilosa</em> weed (relative dominance of 36.16%) but led to an increase in the dominance of <em>Chromolaena odorata</em> (relative dominance of 38.7%). <em>C</em>.<em> pubescens</em> ground cover could also maintain stable soil temperature and moisture, and increase P, K, Ca, and Mg soil content by 100%, 100%, 43.6%, and 48.3%, respectively. Furthermore, pepper plants grown with <em>C. pubescens</em> exhibited 25%, 23.7%, and 16% higher chlorophyll a, total chlorophyll, and carotenoid content, respectively, compared to those grown without the cover crop.</p>

Behaviour of ionic herbicides in different forestry soils derived from volcanic ash

Background: Weed control has been one of the most significant factors in forest establishment practices that can improve biomass production, and herbicides represent the most effective and convenient way to control weeds. The environmental concern about herbicides in this industry is because the herbicide-treated area is often located near water reservoirs or areas where rivers and creeks originate. This study aimed to determine the adsorption and degradation behaviours of seven ionic herbicides used in forestry production in five Chilean forestry soils and their relation to the leaching and to generate information to validate environmental predictive models. Methods: Adsorption and degradation of ionisable herbicides such as simazine, terbuthylazine, hexazinone, metsulfuron-methyl, indaziflam, flazasulfuron and glyphosate were studied in Andisol, Ultisol, Inceptisol, Entisol and Alfisol forestry soils, to be related to their leaching in 100-cm high and 11-cm diameter soil disturbed lysimeters. Herbicides were quantified using high-pressure liquid chromatography and gas chromatography. Relationships between soil physicochemical properties, herbicide adsorption and degradation, and herbicide leaching were determined. Results: In decreasing order, the herbicides were mobile in Entisol>Alfisol>Ultisol>Inceptisol>Andisol soils. On the other hand, the more leachable herbicides, from high to low, were: hexazinone, metsulfuron-methyl, simazine, glyphosate, terbuthylazine, flazasulfuron and indaziflam. The last two herbicides were not detected below 60 cm soil depth. In general, the maximum soil depth herbicide reached and the percentage mass leached up to 90 cm soil depth were inversely related to soil adsorption (1/Kd), soil porosity, humidity, silt, aluminium, and calcium soil content. Herbicide degradations were generally faster than referential published values. Conclusions: The environmental coefficients of ionic herbicides were more related to soil properties than their physicochemical properties. Persistence of herbicides in soil was smaller than that commonly reported in other studies or international databases and soil adsorption averages were generally higher than international reference values. The stronger relationship between ionic herbicide behaviour and forestry soil properties endorses the requirement to determine the environmental herbicides parameters in situ, avoiding using parameters estimated in other soils.

Long-term potato response to different irrigation scheduling methods using saline water in an arid environment

Crops’ water requirement is generally higher than the annual average precipitation in arid environments characterized by scarce freshwater resources. While using saline water for irrigation can help sustain agriculture in water-stressed regions, several challenges arises concerning productivity and soil salinization. However, adoption of efficient irrigation techniques such as drip irrigation, irrigation scheduling, and deficit irrigation can help optimize water productivity and mitigate salinity problems in irrigated agriculture. In southern Tunisia, potato is considered among the main cultivated horticultural crops due to its high economic value while it is considered as a crop sensitive to salinity. This crop (cv. Spunta) was the subject of long-term studies (2002–2020) conducted during the fall period in the arid region of Médenine. The crop response to full and deficit irrigation with saline water was assessed for several seasons under contrasting climatic conditions. Scheduling using the soil water balance (SWB) method consisted of the total and/or partial replacement of accumulated crop evapotranspiration (ETc), as derived from climatic data and crop coefficients. The impact of decreasing amounts of irrigation waters on crop yield and soil salinity with waters having a salinity ranging between 3 and 7 dS m−1 was evaluated. Results showed improvements in yield (30% to 37%) obtained with the SWB strategy under actual farming conditions, supporting the use of this strategy for irrigation. Appropriate scheduling also seems to be a key element in saving water (15%–22%) and in reducing risks of soil salinization. In the dry environment of southern Tunisia, optimum supply seems to correspond to a replacement of 100% to approximately 70%–80% of ETc. Applying such irrigation levels resulted in a lower salinity buildup in the root zone and higher crop water productivity. Natural salt leaching seems to be more effective under a more humid soil profile. Yield decreases and soil salinity increases almost linearly (r2 = 0.60) with decreasing irrigation water amounts. Future work should focus on the integration of management practices when using saline water. Investigating the relationship and interaction between irrigation amounts, cultivar, fertilizer supply, and salt leaching will help in resolving productivity and environmental issues.

Insight into flavor difference of cherry (Prunus avium L.) grown in facility environment and outdoors through metabolomics and correlation analysis

The flavor profiles of cherries cultivated in greenhouse and those grown in open fields show significant variations, however, the underlying flavor-contributing factors remain unidentified. Hence, a joint investigation with widely targeted metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis for the Russia 8 and Tieton cherry cultivars was conducted using UPLC-MS/MS and GC × GC-TOFMS to clarify the flavor differences of open-air and greenhouse-grown cherries. The study found that open-air cultivation could lead to the accumulation of non-volatile flavor substances and prompted appearance of higher acidity, astringency, plum-like flavor, and fresh herb notes; most of differential metabolites were significantly positively correlated with astringency, plum-like flavor and bitterness. Through correlation analysis and path analysis, potential flavor components and key important pathways contributing to flavor disparities were provided, and light intensity, soil moisture content, temperature and humidity were inferred as the main factors affecting the flavor profiles of open-air and greenhouse-grown cherries.

Fungal Diversity in an Undisturbed Andean Páramo Soil in Quimsacocha (Ecuador).

The Andean Páramo is an environment known for its high biodiversity; however, due to its remote location and difficult access, it is still relatively poorly studied. The aim of this work was to explore the fungal biodiversity of Ecuadorian Páramo soils in the undisturbed natural reserve of Quimsacocha through ITS metabarconding with an MiSeq platform. This analysis revealed the presence of 370 fungal Amplicon Sequence Variants (ASVs), mainly composed by Ascomycota, Mortierellomycota and Basidiomycota. The biodiversity had a great variability among the 19 samples, but the soil humidity proved to be a significant driver of diversity in the relatively dry environment of Páramo. Some of most abundant fungal genera have important relationships with plant roots. This work represents the first glimpse into the complex biodiversity of soil fungi in this understudied area, and further studies will be needed to better understand the fungal biodiversity in this region, together with the development of necessary measures of environmental protection.

Litter input promoted dissolved organic carbon migration in karst soil

Litter input is crucial for enhancing carbon sequestration in karst ecosystems. While previous studies have linked litter input to carbon storage in karst soils, the unique conditions of southwest China’s karst regions, characterized by high precipitation and rapid groundwater dynamics, pose challenges to understanding dissolved organic carbon (DOC) migration under litter cover. This study investigated how different litter types from karst-adapted vegetation affect DOC migration and loss in karst soils, addressing the gaps in carbon sequestration evaluations during vegetation restoration. Artificial soil columns were used to monitor the natural rainfall over one hydrological year. The results indicated that litter cover increased the soil carbon dioxide (CO2) concentration and enhanced DOC leaching and migration, with the highest DOC loss observed in the tree litter treatment (2614.50 mg) and the lowest in the shrub litter treatment (1844.13 mg). These differences were attributed to the synergistic interaction between rainfall and litter characteristics, which accounted for 29.94 % of the variation in soil DOC leaching after litter input. Regression analyses indicated that DOC leaching under litter cover was mainly affected by the soil CO2 concentration, temperature, humidity, rainfall, and litter decomposition duration. In summary, litter cover significantly intensified DOC migration and loss in karst soils. This study provides valuable insights into vegetation restoration and reconstruction, particularly in karst regions.

IoT-BASED ROBOTIC SYSTEMS INTEGRATING ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE CROP PROTECTION IN AGRICULTURE

Advancements in technology, including the Internet of Things (IoT) and Artificial Intelligence (AI), greatly impact agriculture. The study investigates the statistical applications of AI-driven robotic devices based on the IoT as a basic emphasis. Using manual effort and chemical fertilizers, traditional farming can be highly attributed to inefficiency, health problems and environmental effects. The paper proposes an AgriBotIQ, a revolutionary platform that uses robotics based on IoT to monitor and analyse with accurate participation in plant management. Autonomous robots can collect information based on plants and their habitats by using imaging devices and sensors like soil moisture, humidity, temperature, and many others. Machine learning (ML) algorithms search the database for anomaly detection, threats, and crop trends. To identify the crops that are diseased or healthy, ML is integrated with computer vision. The suggested AgriBotIQ also eliminated weeds, boosting the output by neglecting unneeded waste and chemicals. The emerging IoTs have allowed better remote plant monitoring in more versatile and précised. Overall productivity and protection of crops are possible by statistical analysis and real-time notifications of the proactive decisionmaking outcomes. By combining IoT and AI, the future agricultural crop security will improve greatly.

Development of SADS (Soil and Air Detector System) to Support Palm Oil Industries in Indonesia

The issue of environmental damage due to the expansion of palm oil plantations in Indonesia has become a global issue. It has often hampered the development of the downstream palm oil industries in Indonesia. The European market was once closed to palm oil and derivative products from Indonesia. In fact, this industry has become a source of income and employment for millions of Indonesians. Therefore, palm oil industry must be supported with sustainable efforts. One of them is developing SADS (Soil and Air Detector System) model. Which is a tool for detecting soil and air environmental conditions around palm oil plantations. The parameters are soil PH, CO2 levels, temperature and humidity, and sunlight. The measurement results from this tool can be accessed and displayed in real-time via the internet. This is research aims to build a smart system as a solution to environmental problems around palm oil plantations. This is useful for knowing and observing the condition of palm oil plantations and as a basis for taking mitigation actions if the condition is in a critical state

Update on the geographical distribution of freshwater crabs of the Pseudothelphusidae family in the semi-arid region of northeastern Brazil

The humid forest zones of northeastern Brazil are recognized as endemic hotspots for pseudothelphusid crabs. In this study, we report new occurrence records of the pseudothelphusids, Fredius ibiapaba and Kingsleya attenboroughi, in humid soil and streams within humid forests of the semi-arid region of northeastern Brazil. These new records expand the geographical distribution of these crabs, highlighting their potential to inhabit humid forests throughout this region. Furthermore, this information indicates that these crabs, especially K. attenboroughi, are not confined to slopes and patches of humid forests in highland swamps but can also extend to springs in other areas of the Brazilian semi-arid region.

Effect of Abiotic Factors on Nectar Quality and Secretion of Two Early Spring Species, Galanthus nivalis L. and Helleborus niger L.

Floral nectar is mainly a reward in the form of food for pollinators. In early spring, when snow can still be present, pollinators have trouble finding food. The composition and productivity of nectar in flowers play an important role in a pollinator’s life. It is known that low temperatures and lower humidity cause lower nectar secretion. Some studies have also shown that the quality of nectar can differ because of lower temperatures. In our research, we analysed whether abiotic factors affect nectar secretion, as well as the nectar composition of the early spring plant species Galanthus nivalis L. and Helleborus niger L. in February 2024. The study was conducted in two locations in nature. Nectar from H. niger was sampled in Tomišelj, Slovenia, whereas nectar from G. nivalis was sampled in Ljubljana, Slovenia. On four different days at three different times of day, we sampled nectar from flowers using microcapillaries. In total, we sampled 48 nectar samples from one species. We analysed soil humidity and temperature, air temperature and humidity, and UVB radiation. Our results show that nectar productivity is highest in the morning for both species. H. niger has sucrose-dominant nectar, while G. nivalis has hexose-dominant nectar. Proline, which is an important amino acid for bees, has the highest level in both species, as does the phenolic compound rutin. Environmental factors do affect nectar secretion. Soil and air temperature affect G. nivalis nectar secretion, while soil humidity affects H. niger nectar secretion. Soil and air temperature also have an effect on higher levels of sugars in both researched nectars. UVB, air humidity, and air and soil temperature seem to have an effect on phenolic compounds, but abiotic factors do not affect amino acids.

Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity

Monitoring Soil Temperature and Humidity in an IoT-Based Green House to Improve Plant Management

This study aims to develop and validate a soil temperature and humidity monitoring system using DHT 11 and YL 69 sensors connected to an ESP 8266 microcontroller. Calibration was performed by comparing sensor data with a reference instrument, followed by regression analysis to determine sensor accuracy. Validation involved calculating the standard deviation and accuracy of the sensor measurements. Temperature and soil humidity monitoring was conducted over five days in September, with average temperature measurements ranging from 22.4°C to 32.6°C and soil humidity ranging from 66.47% to 88.78%. The results showed that the temperature sensor had an average accuracy of 99.19%, while the humidity sensor had an average accuracy of 97.95%. The low standard deviation values for both sensors indicate good measurement consistency. These findings align with the literature emphasizing the importance of high-performance sensors for environmental monitoring in smart agriculture systems. The implementation of this system is expected to enhance the efficiency and effectiveness of agricultural management and support the sustainability of agricultural practices.