IoT and environmental monitoring in mushroom cultivation

The introduction of the Internet of Things (IoT) in the mycoculture sector is radically transforming traditional cultivation practices, offering unprecedented opportunities for controlling and optimizing environmental parameters. This article explores in depth how IoT technologies are revolutionizing environmental monitoring in mushroom cultivation, analyzing the concrete benefits, practical implementations, and measurable results that this innovation is bringing to the sector.

IoT in the Context of Mycoculture

The Internet of Things, commonly abbreviated as IoT, represents one of the most significant technological evolutions of recent decades in the field of agriculture and controlled cultivation. In the specific context of mushroom cultivation, IoT refers to the implementation of a network of connected sensors that collect, transmit, and analyze environmental data critical for the optimal growth of mycelium and fruiting bodies. This technology allows for unprecedented control over the variables that directly influence productivity and harvest quality.

Definition and Fundamental Components of IoT Applied to Cultivation

The IoT infrastructure for mushroom cultivation consists of several interconnected elements that work in synergy to create a comprehensive and automated monitoring system. Environmental sensors represent the first link in the chain, devices specialized in measuring specific parameters such as temperature, relative humidity, CO2 concentration, lighting, and air composition. These sensors are typically strategically distributed within the cultivation environment to ensure homogeneous and representative coverage of the actual conditions.

Communication gateways constitute the second fundamental component, responsible for collecting data from the sensors and transmitting it to cloud platforms or local processing systems. The most commonly used communication technologies include Wi-Fi, LoRaWAN, Zigbee, and NB-IoT, each with specific advantages in terms of range, power consumption, and cost. The choice of communication technology depends on factors such as the size of the facility, the building structure, and data update frequency requirements.

Software analysis platforms represent the brain of the IoT system, where raw data is processed, stored, and transformed into usable information. These platforms often integrate machine learning algorithms capable of identifying patterns, predicting trends, and generating automatic alerts when parameters deviate from optimal ranges. The user interface, generally accessible via web or mobile application, allows growers to view the status of their crops in real time and intervene promptly when necessary.

Measurable Advantages of IoT Implementation

The data presented in the table clearly illustrates the significant improvements achievable through the implementation of IoT systems in mushroom cultivation. The reduction of critical temperature fluctuations stands at around 92%, a particularly important result considering that even minor variations can seriously compromise mycelium development and the formation of fruiting bodies. Similarly, the ability to maintain relative humidity within an extremely narrow range (±2% of the target value) represents a fundamental advancement compared to traditional methods, where oscillations can easily exceed 10-15%.

Critical Environmental Parameters Monitorable with IoT

Mushroom cultivation requires precise control of numerous environmental parameters that directly influence all stages of the growth cycle, from substrate inoculation to fruiting. The implementation of IoT systems allows not only for monitoring these parameters with unprecedented precision and frequency but also for understanding their interrelationships and cumulative effects on productivity and harvest quality.

Temperature: The Fundamental Parameter

Temperature is undoubtedly one of the most critical factors in mushroom cultivation, directly influencing mycelium metabolism, substrate colonization speed, fruiting initiation, and the development of fruiting bodies. Different mushroom species have specific thermal requirements, and often within the same species, there are different needs between the vegetative and reproductive phases.

Advanced Temperature Monitoring with IoT Sensors

IoT systems for temperature monitoring typically employ resistive temperature detector (RTD) type sensors or thermistors, characterized by high precision (±0.1°C) and long-term stability. These sensors are strategically placed to measure not only air temperature but also the temperature of the cultivation substrate, which can differ significantly due to the metabolic activity of the mycelium. The substrate temperature during the colonization phase can be 2-5°C higher than the ambient temperature due to mycelium metabolic activity, data that traditional measurement methods often fail to adequately detect.

Advanced IoT systems often integrate predictive models that, by analyzing historical temperature trends along with other parameters, are able to anticipate potential criticalities and activate preventive countermeasures. For example, a rapid increase in substrate temperature associated with a drop in relative humidity might indicate excessive metabolic activity that, if unchecked, could lead to overheating and damage to the mycelium. In such circumstances, the system can automatically activate cooling systems or increase ventilation to bring parameters back within safe ranges.

Relative Humidity and Substrate Water Content

Humidity represents another fundamental parameter in mushroom cultivation, with requirements that vary significantly between different growth stages and among different species. While insufficient humidity can lead to mycelium dehydration and the development of deformed or stunted fruiting bodies, excessive humidity creates favorable conditions for pathogen development and can interfere with the evaporation processes that trigger fruiting.

Humidity Measurement Technologies in IoT

Modern IoT systems employ relative humidity sensors based on capacitive or resistive principles, capable of accurately measuring values between 0% and 100% with typical accuracy of ±2%. These sensors are generally paired with temperature sensors, forming so-called "combo sensors" that provide coordinated measurements of the two fundamental parameters. The optimal relative humidity for fruiting in most edible mushroom species is between 85% and 95%, an extremely high range that requires particularly precise control.

In addition to air humidity, more advanced IoT systems also monitor substrate water content through soil moisture sensors or via indirect measurements based on weight. This measurement is particularly important during the colonization phase, when the mycelium is actively decomposing the substrate and consuming the water resources contained within it. A decrease in water content below specific critical thresholds can signal the need for rehydration interventions or modifications to environmental conditions to reduce evaporation.

IoT systems for humidity control typically integrate actuators for activating humidifiers, misters, or ventilation systems that allow maintaining values within the desired ranges. The automation of these processes not only reduces the workload for the operator but ensures a stability that would be impossible to achieve with manual adjustments. Humidity fluctuations are among the main causes of morphological anomalies in fruiting bodies, such as cracked caps, elongated stems, or incomplete development, problems that can be significantly reduced through precise and constant control.

CO2 Concentration and Ventilation

The concentration of carbon dioxide (CO2) represents a parameter often underestimated in amateur mushroom cultivation, but which is of critical importance for the quality and productivity of professional cultivations. The mycelium in the growth phase actively produces CO2 as a metabolic byproduct, and the accumulation of this gas can inhibit various physiological processes, particularly the initiation of fruiting and the development of fruiting bodies.

CO2 Sensors and Intelligent Ventilation Systems

CO2 sensors used in IoT systems for mushroom cultivation typically use NDIR (Non-Dispersive Infrared) technology, which offers high precision (±50 ppm) and long-term stability. These sensors measure carbon dioxide concentration in the range of 0-5000 ppm, with configurable alarms for thresholds typically between 800 and 1500 ppm depending on the species and growth stage. The CO2 concentration during the fruiting phase should ideally remain below 1000 ppm for most species, while during colonization higher values (up to 5000 ppm) can be tolerated or even beneficial.

More advanced IoT systems integrate CO2 monitoring with automated control of ventilation systems, activating extraction or air exchange when concentrations exceed predefined thresholds. This approach not only maintains optimal conditions for mushroom growth but also optimizes energy consumption by avoiding excessive air exchange when unnecessary. In large facilities, systems can implement differentiated ventilation strategies based on measurements from multiple zones, concentrating interventions where actually needed.

In addition to controlling CO2 concentration, intelligent ventilation systems managed via IoT can optimize other critical aspects of cultivation. The homogeneous distribution of air prevents the formation of stagnant zones where excessive concentrations of CO2 or gradients of humidity and temperature could develop. At the same time, an airflow that is too intense or direct can cause excessive evaporation and mechanical stress to developing fruiting bodies. The most sophisticated IoT systems automatically modulate the speed and direction of airflows based on real-time measurements, creating optimal microclimatic conditions at every point in the cultivation environment.

Practical Implementation of IoT Systems in Cultivation

The implementation of an IoT system for environmental monitoring in mushroom cultivation requires careful planning that considers the specific needs of the facility, the characteristics of the cultivated species, and the available resources. This chapter provides a detailed guide to the design, installation, and configuration phases of a complete IoT system, analyzing the different technological options and their practical implications.

Sensor Network Design

The design of an efficient and effective sensor network represents the first fundamental step for implementing an IoT system in mushroom cultivation. Proper design must ensure complete and representative coverage of the cultivation environment, considering the inevitable microclimatic variations that occur in any space, especially large ones.

Determining the Number and Placement of Sensors

The number of sensors needed depends on several factors, including the size of the cultivation environment, the presence of obstacles or shadow zones, the natural variability of environmental parameters, and the desired granularity of information. As a general rule, for environments up to 50 m², one sensor every 10-15 m² is sufficient, while for larger spaces the density can be reduced to one sensor every 20-25 m², provided the distribution is homogeneous and strategically planned.

Sensor placement must consider both height and horizontal distribution. Regarding height, temperature and humidity sensors should be placed at the height of the cultivation beds or structures, where the mushrooms actually grow, rather than at ceiling or floor level. CO2 sensors, on the other hand, can be placed at different heights to detect any gas stratification. The horizontal distribution of sensors should cover both central and peripheral zones, with particular attention to potentially critical areas such as those near doors, windows, or climate control systems.

In addition to standard environmental sensors, more complete systems include specialized sensors for specific measurements. Substrate moisture sensors, generally in the form of probes to be inserted directly into the cultivation material, provide valuable information on the water status of the growth medium. Differential pressure sensors can monitor the efficiency of air filtration systems, particularly important in facilities requiring high sterility standards. Airflow sensors integrated into ventilation systems allow verifying that flow rates are maintained within design values, promptly identifying any obstructions or malfunctions.

Choice of Communication Technologies

The choice of communication technology for the IoT system represents a critical decision that directly influences the reliability, operating costs, and scalability of the facility. The available options vary in terms of range, power consumption, transmission speed, and costs, and the optimal choice depends on the specific characteristics of the cultivation facility.

Comparative Analysis of Communication Technologies

Wi-Fi represents a common solution for small and medium-sized facilities, especially when a network infrastructure is already available. The main advantages include high transmission speed, wide availability, and relatively contained costs. However, Wi-Fi has limitations in terms of range, especially in environments with many dividing walls or interference, and relatively high power consumption which can be a problem for battery-powered sensors.

LoRaWAN (Long Range Wide Area Network) has established itself as one of the preferred technologies for IoT applications in agriculture and cultivation thanks to its exceptional range (up to 15 km in rural areas), very low power consumption, and excellent penetration capacity in buildings and structures. LoRaWAN sensors can operate for years on a single battery, significantly reducing maintenance costs. The main disadvantage is the low transmission speed, which however is generally sufficient for environmental monitoring applications where data is sent at intervals of minutes rather than continuously.

The choice of communication technology should consider not only the technical characteristics but also practical aspects such as the availability of power for the sensors, the need for mobility or reconfiguration, and the total cost of ownership including maintenance and updates. For most mushroom cultivation applications, LoRaWAN represents the best compromise between range, power consumption, and costs, especially for medium and large-sized facilities where sensor distribution covers an extensive area.

Integration with Existing Control Systems

One of the most complex aspects in implementing IoT systems in mushroom cultivation is the integration with existing control systems, such as thermostats, humidifiers, climate control systems, and lighting systems. Effective integration allows not only monitoring environmental parameters but automatically intervening to maintain them within desired ranges, creating a fully automated closed-loop control system.

Communication Protocols and Standard Interfaces

Integration between IoT systems and environmental control equipment typically requires the use of standardized communication protocols that allow the exchange of data and commands between devices from different manufacturers. The most widespread protocols in the sector include Modbus, BACnet, and MQTT, each with specific advantages and application areas.

Modbus, originally developed in the 1970s, remains one of the most widespread protocols in the field of industrial automation thanks to its simplicity and reliability. Available in serial (RS-485) and TCP/IP versions, Modbus allows a master device to read and write registers in slave devices, such as thermostats, humidity controllers, or fan inverters. The simplicity of the protocol makes it ideal for basic integrations, although it lacks some advanced functionalities present in more modern protocols.

BACnet (Building Automation and Control Networks) is a standard developed specifically for building automation that has gained wide diffusion in climate control and environmental control applications. Unlike Modbus, BACnet includes standardized object models for different types of devices (thermostats, sensors, actuators) and advanced services such as alarm notifications and scheduling. BACnet is particularly suitable for large mushroom cultivation facilities with complex HVAC systems (Heating, Ventilation and Air Conditioning).

MQTT (Message Queuing Telemetry Transport) represents the most modern protocol among those mentioned, developed specifically for IoT applications. Based on a publish-subscribe architecture, MQTT is extremely efficient in terms of bandwidth and resources, ideal for connections with limited bandwidth or low-power devices. Its flexibility and simplicity make it particularly suitable for cloud integrations and applications involving devices from different manufacturers.

Practical integration typically requires the use of gateways capable of translating between the different protocols used by the IoT sensors and the existing control systems. These gateways collect data from the sensors (often via LoRaWAN, Zigbee, or Wi-Fi) and convert it into the protocol understood by the control systems (such as Modbus or BACnet). Similarly, they receive commands from the control systems and transmit them to the appropriate actuators. The choice of the right gateway is crucial to ensure smooth and reliable integration between the IoT system and the existing infrastructure.

Data Analysis and Artificial Intelligence in Cultivation

The true power of IoT systems in mushroom cultivation lies not simply in data collection, but in its transformation into actionable information and concrete actions through advanced analysis and the application of artificial intelligence techniques. This chapter explores the methodologies and technologies for extracting value from the collected data, transforming environmental monitoring from a reactive activity into a predictive and proactive system.

Statistical Processing of Environmental Data

The raw data collected by IoT sensors contains a huge amount of information, but often presents noise, anomalous values, and complex patterns that require appropriate statistical processing to be correctly interpreted. Data processing techniques applied to cultivation systems include filtering, normalization, interpolation, and time series analysis.

Data Pre-processing and Cleaning Techniques

Filtering represents one of the first stages of data processing, aimed at reducing measurement noise without losing significant information. Common techniques include moving average, which replaces each value with the average of surrounding values in a defined time window, and the Kalman filter, a more sophisticated algorithm that combines multiple measurements to produce optimal estimates. Appropriate filtering can significantly improve data reliability without masking real variations in environmental parameters.

The identification and management of anomalous values (outliers) is another critical phase of pre-processing. Anomalous values can derive from temporary sensor malfunctions, electromagnetic interference, or transitory conditions that are not representative. Statistical techniques such as the Grubbs' test or isolation forest can automatically identify these values, which can be replaced with more reliable estimates through interpolation or predictive models. Appropriate outlier management is particularly important to avoid triggering false alarms or leading to incorrect decisions based on non-representative data.

Spatial interpolation allows creating continuous maps of environmental parameters from discrete measurements taken by sensors distributed in the environment. Techniques such as kriging or IDW (Inverse Distance Weighting) interpolation can generate visualizations showing gradients and critical zones that might not be evident when examining individual measurement points. These maps are particularly useful for identifying zones with unfavorable microclimates that might require specific interventions, such as repositioning fans or modifying the arrangement of cultivation beds.

Time series analysis applied to environmental data allows identifying recurring patterns, long-term trends, and cause-effect relationships between different parameters. Techniques such as seasonal decomposition can separate recurring daily fluctuations (due for example to the periodic switching on of climate control systems) from longer-term trends (such as the progressive increase in substrate temperature during colonization). The identification of correlations between different parameters can reveal causal relationships not immediately evident, such as the impact of humidity variations on the temperature perceived by the mycelium or the influence of CO2 concentration on nutrient absorption efficiency.

Machine Learning for Predictive Control

Machine learning techniques represent the most advanced evolution in data analysis for mushroom cultivation, allowing not only to describe and interpret environmental data but to predict future behaviors and automatically optimize control parameters. The application of machine learning to mycoculture is rapidly transforming the approach from reactive to predictive, with significant improvements in terms of yield, quality, and efficiency.

Predictive Models for Yield and Harvest Quality

Regression models represent one of the most directly applicable machine learning approaches for predicting mushroom yield. Trained on historical data including environmental parameters and results from previous harvests, these models can predict the expected yield based on current and forecasted conditions. Common algorithms include multiple linear regression, decision trees, random forest, and artificial neural networks, each with specific strengths and weaknesses.

Recurrent Neural Networks (RNN), particularly LSTM (Long Short-Term Memory) variants, are especially suited for analyzing sequential data like time series of environmental parameters. These models can capture long-term dependencies in the data, recognizing for example that a specific sequence of temperature and humidity variations during the pre-fruiting phase tends to precede abundant fruiting or, conversely, the development of anomalies. LSTM models can predict yield with lead times of up to 7-10 days, allowing timely corrective interventions when forecasts indicate suboptimal results.

In addition to quantitative yield, classification models can predict qualitative aspects of the harvest, such as average mushroom size, cap/stem ratio, coloration, and the presence of defects. These models, typically based on algorithms like SVM (Support Vector Machines) or convolutional neural networks, analyze not only environmental parameters but also images of developing fruiting bodies captured through computer vision systems. The integration of environmental and visual data allows building extremely accurate models capable of predicting qualitative characteristics that directly influence the commercial value of the product.

The practical implementation of machine learning systems requires adequate infrastructure for model training and inference. For real-time applications, more complex models may require the use of GPUs for accelerated processing, especially when involving image analysis or high-resolution time series. The most common approach involves initial model training on cloud infrastructure with subsequent deployment on local edge devices for real-time inference, thus balancing the needs of computational power with those of latency and independence from internet connectivity.

Decision Support Systems

Decision Support Systems (DSS) integrate data, analytical models, and domain-specific knowledge to assist growers in the decision-making process, suggesting specific interventions based on the analysis of current and forecasted conditions. In the context of mushroom cultivation, these systems can recommend adjustments to environmental parameters, modifications to management protocols, or specific interventions to prevent problems or maximize yield.

Architecture and Components of a DSS for Mycoculture

A DSS for mushroom cultivation typically comprises four main components: the data acquisition module, the analysis and modeling module, the knowledge base, and the user interface. The data acquisition module collects information from various sources, including IoT sensors, control systems, manual operator inputs, and external data such as weather forecasts. This data is validated, normalized, and integrated into a consistent format ready for analysis.

The analysis and modeling module applies statistical and machine learning algorithms to extract meaningful information from raw data. This module typically includes predictive models for yield and quality, diagnostic models to identify causes of problems, and prescriptive models to recommend specific interventions. The most advanced prescriptive models use optimization techniques like linear programming or genetic algorithms to identify the combination of parameters that maximizes multiple objectives, such as yield, quality, and energy efficiency.

The knowledge base contains the domain-specific knowledge necessary to correctly interpret the data and generate relevant recommendations. This includes information on the specific needs of different mushroom species, critical thresholds for different environmental parameters, standard operational protocols, and scientifically validated cause-effect relationships. The knowledge base can be structured as a rule-based expert system, a formal ontology, or a combination of different approaches, and is typically developed in collaboration with mycology experts and experienced growers.

The user interface presents information and recommendations in an intuitive and usable format, typically through web dashboards or mobile applications. Visualizations include real-time charts of environmental parameters, performance indicators (KPIs), alerts and notifications, and specific recommendations with related justifications. A well-designed interface also allows for operator feedback, who can confirm, modify, or reject the system's recommendations, thus contributing to the continuous improvement of models through active learning.

Case Studies and Practical Applications

The implementation of IoT systems in mushroom cultivation has produced tangible and measurable results in various contexts, from small family businesses to large industrial facilities. This chapter presents detailed case studies illustrating the practical application of the technologies described in previous chapters, analyzing the challenges faced, the solutions implemented, and the results obtained.

Case Study 1: IoT Conversion of a Traditional Pleurotus ostreatus Facility

A company located in the province of Brescia represents an emblematic example of the conversion of a traditional Pleurotus ostreatus (oyster mushroom) cultivation facility through the implementation of IoT technologies. With a cultivation area of 800 m² distributed across four climate-controlled rooms, the company had already achieved a good level of efficiency through traditional methods but faced significant challenges in the stability of environmental parameters and the control of final product quality.

Implementation and Results

The implementation of the IoT system occurred in three distinct phases over six months. The first phase involved the installation of an environmental sensor network comprising 32 temperature/humidity sensors, 8 CO2 sensors, and 16 substrate moisture sensors, communicating via LoRaWAN technology. The sensors were distributed according to a grid design ensuring homogeneous coverage of all cultivation rooms, with higher density in critical zones such as those near access doors and climate control systems.

The second phase involved the integration of existing control systems through Modbus-TCP gateways allowing bidirectional communication between the IoT system and the already installed thermostats, humidifiers, and fans. This integration required precise mapping of all control points and the configuration of automation logics based on real-time data from the sensors. The integration allowed the transition from control based on fixed setpoints to adaptive control that continuously modifies operational parameters based on actual measured conditions in the environment.

The third phase implemented a machine learning-based decision support system, trained on the company's historical data integrated with manual measurements of yield and quality from previous harvests. The system can predict expected yield with a 7-day lead time with 89% accuracy, and identify conditions predisposing to the development of specific qualitative defects such as cracked caps or excessively elongated stems.

The results obtained by "Funghi Prelibati" clearly demonstrate the tangible benefits of IoT implementation even in an already efficient context. The 24.7% yield increase represents a significant improvement that exceeds initial expectations, while the reduction in waste due to quality defects has allowed the company to access more profitable market segments requiring high-quality standards. The energy savings, although not the primary goal of the project, constitutes an important additional benefit in a context of rising energy costs.

Case Study 2: IoT Facility for Cultivation of Rare and Premium Species

A research center in Bologna developed a fully automated pilot facility for the cultivation of rare and premium mushroom species, such as Hericium erinaceus (lion's mane), Grifola frondosa (maitake), and Sparassis crispa (cauliflower fungus). The cultivation of these species presents specific challenges due to the scarcity of established knowledge about their optimal environmental requirements and their greater sensitivity compared to more common commercial species.

Experimental Approach and Results

The pilot facility was designed as a modular system composed of 12 independent cultivation units, each equipped with a complete environmental monitoring and control system. Each unit is equipped with temperature, humidity, CO2, lighting (intensity and spectrum), and air composition sensors (with particular attention to volatile organic compounds emitted by the mycelium), in addition to computer vision systems for non-invasive monitoring of fruiting body development.

The experimental approach involved executing cultivation cycles with different combinations of environmental parameters according to a response surface experimental design, which allows mathematically modeling the relationship between input variables (environmental parameters) and output variables (yield, quality, growth time). For each species, 45 different combinations of temperature, humidity, CO2, and lighting were tested, with each condition replicated three times to ensure statistical significance of the results.

The collected data was analyzed through machine learning techniques to identify not only the optimal conditions for each species but also the interactions between different parameters and the critical thresholds beyond which significant drops in yield or quality occur. The analysis revealed that for Hericium erinaceus the interaction between temperature and humidity is significantly more important than for other species, with extremely narrow optimal windows that explain the difficulties encountered in the traditional cultivation of this species.

The project results have allowed defining scientifically validated cultivation protocols for species that were previously considered marginal or difficult to cultivate. The yield of Hericium erinaceus of 4.8 kg/m² represents an absolute record for this species, exceeding by over 60% the typical results reported in literature. At the same time, the identification of the most critical parameters for each species allows optimizing monitoring and control resources, concentrating efforts where actually necessary.

Economic Considerations and Return on Investment

The implementation of IoT systems in mushroom cultivation represents a significant investment that requires careful economic evaluation. This chapter analyzes the costs associated with the different system components, the expected economic benefits, and the methodologies for calculating return on investment, providing concrete tools to support the investment decisions of growers and investors.

Implementation Cost Analysis

The implementation costs of an IoT system for mushroom cultivation can be divided into three main categories: hardware costs, software costs, and installation and configuration costs. The quantification of these costs depends on numerous factors, including the size of the facility, the desired level of automation, the complexity of integration with existing systems, and the choice of proprietary or open-source technologies.

Cost Breakdown by Component

Hardware costs include sensors, communication gateways, actuators, network infrastructure, and power systems. Sensors typically represent the most significant cost item, with prices ranging from 50 to 300 euros per unit depending on technology, precision, and functionality. Communication gateways have costs between 200 and 1000 euros depending on the supported technology and edge processing capacity. Actuators for controlling climate control, humidification, and ventilation systems can represent a significant investment, especially if the project involves replacing existing incompatible equipment.

Software costs include licenses for data management platforms, advanced analysis algorithms, decision support systems, and user interface applications. Complete commercial solutions can cost from 5,000 to 50,000 euros depending on complexity and the number of monitored points, while solutions based on open-source software require lower license investments but higher costs for customization and maintenance. Custom development costs can range from 20,000 to 100,000 euros for medium-sized facilities, depending on specific requirements.

Installation and configuration costs include system design, physical installation of components, software configuration, integration with existing systems, and staff training. This item typically represents 30-50% of the total project cost and depends heavily on the complexity of the facility and the experience of the supplier. For large facilities, installation costs can be reduced through a modular approach that distributes implementation over multiple phases.

In addition to initial costs, it is important to consider recurring operational costs, which include maintenance, software updates, device energy consumption, and any subscriptions for cloud services or telecommunications. These costs typically represent 10-15% of the initial investment annually, although they can vary significantly based on the scale of the facility and the technologies chosen. Systems based on open-source technologies and standard protocols tend to have lower operational costs thanks to reduced dependence on specific suppliers and greater flexibility in maintenance.

Economic Benefits and ROI Calculation

The economic benefits resulting from the implementation of IoT systems in mushroom cultivation can be divided into direct benefits, easily quantifiable in monetary terms, and indirect benefits, which although contributing to company profitability are more difficult to measure precisely. A proper economic evaluation must consider both categories to provide a complete picture of investment profitability.

Measurable Direct Benefits

The increase in yield typically represents the most significant economic benefit, with documented improvements ranging from 15% to 30% depending on initial conditions and the completeness of implementation. For a 500 m² facility with a base yield of 20 kg/m²/year and a selling price of 8 €/kg, a 20% yield increase generates additional revenue of 16,000 €/year (500 m² × 20 kg/m²/year × 20% × 8 €/kg).

The reduction of waste due to quality defects constitutes another important direct economic benefit. In the analyzed case studies, waste reduction varies from 30% to 60%, with significant impacts on profitability considering that discarded mushrooms have still absorbed production costs but do not generate revenue. Continuing with the previous example, if the waste percentage is reduced from 12% to 6%, the economic benefit is 4,800 €/year (500 m² × 20 kg/m²/year × 94% salable × 8 €/kg - scenario with 12% waste: 500 m² × 20 kg/m²/year × 88% salable × 8 €/kg).

The reduction in energy consumption, although generally less significant in absolute terms compared to yield increase, contributes to investment profitability. Documented energy savings vary from 15% to 30%, reaching in some cases 40% for particularly inefficient facilities before implementation. For a facility with annual energy consumption of 50,000 kWh and an energy cost of 0.20 €/kWh, a 20% saving equals 2,000 €/year.

The reduction in manual labor represents a further direct economic benefit, particularly important in contexts with high labor costs or difficulties in finding specialized manpower. In the analyzed case studies, the reduction in time dedicated to manual monitoring and adjustment of environmental parameters varies from 25% to 40%, freeing up human resources for higher value-added activities. For a company with two operators dedicated to environmental monitoring, with an hourly cost of 15 €/h and 1,800 h/year per operator, a 30% reduction in time dedicated to these activities equals a saving of 16,200 €/year.

The calculation of return on investment (ROI) is obtained by comparing annual economic benefits with the initial investment. Using the data from the previous example, with an initial investment of 25,000 € (average value for a 500 m² facility) and annual benefits of 48,600 €, the payback period is about 6 months (25,000 € / 48,600 € = 0.51 years). Even considering annual operational costs of 15% of the initial investment (3,750 €/year) and a discount rate of 8%, the net present value (NPV) of the investment over 5 years exceeds 150,000 €, confirming the high profitability of the investment. IoT systems for mushroom cultivation typically show payback periods between 6 and 18 months, making them among the most profitable investments in the agricultural sector.

IoT: The Technology of the Future

The integration of IoT technologies in environmental monitoring for mushroom cultivation represents an inevitable and extremely advantageous evolution for the sector. The systems described in this article allow for unprecedented control of critical parameters for mushroom growth, transforming cultivation from an empirical art to a precise and repeatable science. Documented benefits include significant yield increases, product quality improvements, energy consumption reductions, and optimization of human resource employment.

Despite the initial investment possibly appearing significant, the economic analysis presented clearly demonstrates that IoT systems for mushroom cultivation offer returns on investment among the fastest and most consistent in the agricultural sector, with payback periods typically under 18 months even in medium-sized facilities. The scalability of these solutions makes them accessible to both small family businesses and large industrial facilities, with modular implementation approaches allowing investment distribution over time.

Looking to the future, the integration of IoT with other emerging technologies like artificial intelligence, robotics, and blockchain promises to bring further revolutions in the mycoculture sector. Completely autonomous systems capable of self-regulation and self-optimization, complete supply chain traceability, and extreme personalization of cultivation protocols for specific genetics represent just some of the possibilities opening up. Mushroom cultivation is rapidly transforming from a traditional activity to a high-tech sector, with enormous opportunities for those who embrace these innovations in a timely manner.