The results showed that the maximum yield of wheat fodder was obtained using fluorescent light. Mosallam et al. studied the impact of different light sources such as natural, fluorescent, compact fluorescent, and LED light on barley fodder yield. The duration of light exposure was from 6.0 to 24 hr per day for all selected light sources. The authors concluded that the maximum yield of barley fodder was achieved by LED light with the photoperiod from 12 to 18 hr. Another study describes the effect of red and blue LED light with a 3:1 ratio for a photoperiod of 16 hr and natural light on plant height. The authors observed better growth in plant height with LED light than with natural light. Based on the detailed literature, it has been concluded that the combination of red and blue LED lights is significant for plant photosynthesis, growth, and the development of fodder production in CE facilities. According to the previous studies, the optimum red and blue LEDs ratio was 3 for nutrients uptake, yield, energy, and water use efficiency CEA applications . Researchers and scientists should explore such red and blue LEDs combinations and report their findings in reputed journals for CEFP systems.Sensing, monitoring, and controlling growth parameters inside high-tech hydroponic plant production systems have shown significant technical improvement in modular electronics, low-power consumption, long-range wireless communications, and optimization algorithms. An efficient control system for fodder production under CE with minimum energy inputs requires proper integration and adaptation of the existing automation systems with digital technology such as the Internet-of Things to incorporate real-time data transfer and live monitoring. In large-scale cultivation, measurements from multiple wireless sensors are fed into conventional decision support systems, artificial intelligence algorithms, aeroponic tower garden system or crop growth models using cloud-based streaming systems to increase production efficiency and profit.

Modern commercial indoor growing systems also take advantage of predictive models in their environmental control systems via wireless sensing and monitoring to overcome the difficulties and limitations of conventional timer-based control methods. To our best knowledge, no research has been published on control and automation for CEFP. Therefore, this section provides an overview of wireless automation workflow employing distributed nodes that have been custom-designed for CE based on a powerful micro-controller with LoRa modulation at 868MHz. Sample experiments from a pilot plant greenhouse with IoT hardware and software show the data transmission’s stability, robustness, and reliability in CEFP. Wireless communications between sensors and controllers for monitoring and sending warning messages in CEA systems were adopted in the early 2000s due to their compact size, cost-effectiveness, flexibility, and easy installation. In large-scale commercial production, the number of the sensor nodes, locations of the repeaters, power consumption, operating frequencies, and the distance between transmitters and receivers should be considered for continuous data collection. On the other hand, the applications of live data monitoring and knowledge based and dynamic decision support systems benefit from this data stream for wireless automation and control. Such technology reduces running wire and maintenance between sensors, especially in rural agricultural areas. However, various studies have discussed the limitations of radio wave propagation in the presence of high-density plants like fodder production and therefore suggested wireless sensor networks with IoT functionality.

In IoT-based monitoring systems, the receiver node uploads data on a web server to make them accessible by any client device connected to the worldwide web. Some of the justifications for replacing traditional dataloggers with IoT sensing systems in CEA systems are the lack of data sharing and availability, operator cost and intensity, low spatiotemporal resolution, a lack of data centralization, and organizational structure management in observing the environmental aspects. IoT-based data collection also offers an excellent opportunity for the non-destructive quantification of physiological factors of plants to be shared within the network. For example, historical data collected by IoT sensors can be shared within local grower communities for conducting combined pest and disease management agendas to stop the spreading of the associated damages. These data can also improve cultural practices and prediction analysis in commercial-scale production. LoRa is a leading communication technology for long-range wireless data transfer with low-power consumption in agriculture, allowing the distance between the sender and the monitoring devices to be a few meters to 100 km or more. Several successful applications of this technology for IoT-based monitoring and control in closed-field and open-field crop production in remote locations with no mobile network coverage have been addressed in previous works. The main components of this technology, including intelligent sender nodes, repeaters, receiver nodes, cloud storage, and IoT platform, have been shown in Fig. 9a. With this setup, live data can be viewed and shared with unlimited users and applications from any location. The selection or design of a compact sender node has efficient battery management and can withstand a harsh indoor environment. These modules consist of a programmable micro-controller interfaced with various sensor probes, onboard memory storage, communication modules, and battery management units . For control actuator nodes , sensor data are first fed into secure cloud-based applications before reaching IoT-based controllers.

The architecture shown in Fig. 9c includes several hardware and software layers linked with wires or wirelessly through standard communication protocols such as WiFi and CANBUS. A multi-channel sensor node is shown in Fig. 10a with an IP66 enclosure, WiFi and LoRa antenna, external power supply, and aviation plug connectors that have been custom designed for easy interfacing with various greenhouse sensors probes. There are two separate circuit boards inside the box: one for transmitting sensor and GPS data via LoRa 868 MHz and the other for WiFi communication and data logging on an SD card . The design makes it possible to add new sensing capabilities to the existing wireless networks and external programming with minimum effort. In the same way, defective sensor probes may be easily replaced to ensure the network’s lowest maintenance cost. The connectivity boards are shown in Fig. 10b and c includes all the electronics and sockets necessary to connect the most typical sensors in wireless monitoring of the indoor environment. These are BME280 , DS18B20 , LDR Photoresistor , SX239 , and NEO-7 GNSS modules. The sensor node benefits from the powerful ESP32 and Atmega328P micro-controllers integrated with customized codes for high efficiency and ultra-low power consumption for more robust and fast processing. The sensor node has a DS1337 IC for real-time logging clock and can access dates and times from an available world clock server in a WiFi network. The final log file is saved on a cloud server or the onboard SD card with GPS and time stamps and may include hundreds or thousands of data lines, depending on the data collection frequency and growing season. The sensor node has been tested successfully in multiple CEA applications and has measured, recorded, and transferred data without interruptions. The wireless communication between these transmitter boards and receiver is realized through LoRa technology,868 MHz , and 915 MHz which covers 2~10 km distance in rural areas and is extendable to 100 km with repeaters. By default, these boards have been programmed to read and record measurements every 10 seconds, adjusted according to the growers’ needs.

Data are stored on an onboard mini-SD card or are transferred to an open-source secure cloud database via WiFi connection.Fig. 10d shows the continuity and precision of IoT-based data collection using this sensor for live monitoring of air temperature inside a metal heat control chamber isolated in a concrete basement for connectivity tests. The hybrid data logger system shown in Fig. 10 can be used to evaluate controlled environments concerning the different set-points of microclimate parameters and soil temperature before the actual cultivation. Knowing the reference values for air temperature, RH, VPD, and ST at different growth stages of fodder production, instantaneous plots of the collected data on a mobile app provide insight into the divergence of the environment from ideal conditions. This approach is necessary for decision-making in large-scale productions in which a model of the controlled environment is first built and tested. Fig. 11 shows plots of a data-set collected using the hybrid data logger system from an Agricube model equipped with an electrical heater and a thermoelectric cooling device to alter the environment. The test was designed to study the heat exchange between air and soil bed with the null hypothesis that a linear correlation may exist between air and soil temperature under large environmental temperature changes. More details of the experiment can be found in Shamshiri et al..The proposed data acquisition system, sensor probes, and modular battery packs were placed inside the cube for seven days. Data were collected precisely every 10 minutes and were stored on a private cloud via WiFi connection for IoT monitoring. The same data were also logged on the onboard SD card and could be downloaded using a standard USB port. The plots shown in Fig. 11 confirm the reliability and robustness of the proposed low-cost data logger system to operate on battery under different temperatures, which is a critical factor for long-term evaluation of controlled environment crop production systems, dutch buckets for sale especially in remote areas. This monitoring system could be easily integrated into modular shipping containers for efficient and precise operation in CEFP facilities.

This advanced monitoring technology could be integrated into CEFP to make the system more efficient, automatic, and less labor-intensive. However, minimal research activities have been conducted to investigate the application of IoT for controlled environment fodder production. Muralimohan et al.tested an in-built IoT-based monitoring system remotely through a mobile application for a small-scale fodder production under a controlled environment. The study reported that the proposed setup requires minimal labor for operations and efficient control of indoor environments.The data received from the sensors are sent to the microcontroller and then transferred to the cloud through the Google firebase. The firebase is an interface for communication between the hydroponic system and the mobile application. More research activities need to be executed to analyze the system-level performance of IoT-based monitoring systems to reduce the operational cost and improve production efficiency in CEFP. Precise control of environmental variables in fodder production can contribute to the sustainability of the operation by reducing water, chemicals, and energy demands, while at the same time avoiding disease spread and increasing yield and profit. In general, automation of hydroponic indoor farming environments has to deal with various uncertainties and disturbances that cannot be entirely modeled or implemented by conventional control algorithms and therefore requires adaptive solutions to reduce production costs and improve efficiency. For this purpose, collected data from multiple wireless sensors deployed in different parts of the growth environment should be used with knowledge-based software and dynamic models. Fig. 12 shows a general overview of a model reference adaptive control that updates its parameters for adjusting reference air and root zone temperature values in the controlled system depending on specific growth stages, light conditions, and external variables. In this scheme, the control objective is to maintain inside parameters close to the set-points by minimizing a cost function that derives the error between reference values and the model value to zero. For the case of microclimate parameters, the controller activates ventilation fans and vents, shading covers, heating, or cooling systems to achieve air temperature and RH values that correspond to an optimum vapor pressure deficit. It should be noted that the changes in such system dynamics are highly nonlinear and varies with light conditions, crop growth stage, covering materials, outside conditions, and other disturbances, which require self-tuning of control parameters to cancel out nonlinearities. For this purpose, multiple wireless sensor nodes should be deployed inside and outside the CEA systems to send their measurements to a receiver board.A similar approach could be implemented for CEFP to efficiently control the air temperature, humidity, root-zone temperature, and moisture content. A custom-designed IoT-based controller board can communicate between sensor nodes, end-users, and actuators, sending and receiving manual command signals .