Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

https://www.mdpi.com/1424-8220/19/17/3796/pdf

Precision Agriculture Techniques and Practices: From Considerations to Applications.

With growing food demand worldwide, controlled environment agriculture is an important strategy for crop production year-round. One of the important types of controlled environment agriculture is greenhouses. Key indoor environmental parameters such as carbon dioxide, moisture, lighting, and temperature are required to be maintained for favorable crop growth in greenhouses. Due to lightweight construction and inefficient operation, greenhouses consume more fossil fuel energy in the operation of mechanical systems than other similar sized buildings and have larger carbon footprints. In fact, greenhouses are one of the most energy-intensive sectors of the agricultural industry. Energy consumption in greenhouses is influenced by mechanical systems, indoor environment, crop growth, and evapotranspiration. Therefore, energy simulations help analyze the complex thermal processes in greenhouse operation, and contribute to energy efficient greenhouse operation. This paper reviews existing strategies on energy efficient control operation and state-of-the-art energy simulation for greenhouses. It first discusses strategies for improving energy efficiency in greenhouse control operation by summarizing the studies on energy efficient operation strategies, the control of key greenhouse parameters, sensing network and monitoring systems, along with various control algorithms. Second, the review covers energy modeling of greenhouses by summarizing existing and developed approaches. Finally, this review identifies areas in which future research has the potential to reduce greenhouse energy consumption and carbon footprint.

Energy efficient operation and modeling for greenhouses: A literature review

This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning (ML). Almost all the analyzed works use the feedforward architecture, while the recurrent and hybrid networks are little exploited in the various tasks of the greenhouses. Throughout the document, different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The advantages and disadvantages of neural networks (NNs) are observed in the different applications in greenhouses, from microclimate prediction, energy expenditure, to more specific tasks such as the control of carbon dioxide. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in which systems involve technologies 4.0.

https://www.mdpi.com/2076-3417/10/11/3835/pdf

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

The rapid change of climate, population explosion, and reduction of arable lands are calling for new approaches to ensure sustainable agriculture and food supply for the future. Greenhouse agriculture is considered to be a viable alternative and sustainable solution, which can combat the future food crisis by controlling the local environment and growing crops all year round, even in harsh outdoor conditions. However, greenhouse farms persist many challenges for efficient operation and management. The evolving Internet of Things (IoT) technologies, which encompass the smart sensors, devices, network topologies, big data analytics, and intelligent decision is believed to be the solution in addressing the key challenges facing the greenhouse farming, such as greenhouse local climate control, crop growth monitoring, crop harvesting and etc. This paper reviews the current greenhouse cultivation technologies as well as the state-of-the-art of IoT technologies for smart greenhouse farms. The paper also highlights the major challenges that need to be addressed.

Internet of Things Empowered Smart Greenhouse Farming

Model-based evaluation of greenhouse microclimate using IoT-Sensor data fusion for energy efficient crop production

Robust model predictive control for greenhouse temperature based on particle swarm optimization

Greenhouse Environment dynamic Monitoring system based on WIFI

Greenhouse temperature predictive control for energy saving using switch actuators

The invention provides a treatment method, a treatment device and a treatment system for temperature control and energy conservation. The treatment method for temperature control and energy conservation comprises the following steps: establishing a plurality of corresponding temperature prediction models according to the species of control devices; conducting model identification to the temperature prediction models according to collected history data and confirming all model parameters in the temperature prediction models, and establishing quadratic performance index according to the prediction temperature value output by the temperature prediction models, the deviation of the preset temperature and the energy loss started by the control devices; acquiring the corresponding on-off combination switching sequences of the control devices when the quadratic performance index reaches the minimum value and controlling the switch of the control devices according to the on-off combination switching sequences. The treatment device and the treatment system are used for carrying out the treatment method. According to the embodiment, the treatment method, the treatment device and the treatment system consider the greenhouse temperature and the energy loss in the controlling process of the control devices.

Treatment method, device and system for temperature control and energy conservation

Greenhouses are closed environments that require careful climatic control, which can benefit from a system control method to cope with the high nonlinearity, complex coupling, and robustness of unknown disturbances. This paper presents a general framework for an integral sliding mode controller based on a disturbance observer combined with feedback linearization for a greenhouse temperature and humidity system. The first-principle greenhouse climate model is described as a standard affine nonlinear system. The feedback linearization control law is used to achieve a system consisting of two separate integrator channels for temperature and humidity. System compound disturbances are estimated by applying a sliding mode disturbance observer. Based on the observer, an integral sliding mode control is incorporated to enhance the robustness against uncertainties and guarantee satisfactory tracking performance even when there are unknown estimation errors. The validity and efficacy of the proposed control technique for greenhouse climate tracking were verified by comparison with simulation results obtained using the common sliding mode control method using feedback linearization without the disturbance observer. Based on this comparison, the developed controller shows a faster system response speed, higher control precision, and stronger anti-interference ability. This method can be applied to improve greenhouse climate control systems.

/pdf/sliding-mode-control-based-on-disturbance-observer-for-2jt1z6yqzx.pdf

Shang-Feng Du

Papers

Robust model predictive control for greenhouse temperature based on particle swarm optimization

Greenhouse Environment dynamic Monitoring system based on WIFI

Greenhouse temperature predictive control for energy saving using switch actuators

Treatment method, device and system for temperature control and energy conservation

Sliding Mode Control Based on Disturbance Observer for Greenhouse Climate Systems