Computer vision technology in agricultural automation —A review

A review on monitoring and advanced control strategies for precision irrigation

Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review

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

Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.

https://www.mdpi.com/2073-4395/11/4/667/pdf

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

Model predictive control and its application in agriculture: A review

Automatic carbon dioxide enrichment strategies in the greenhouse: A review

With the increase in greenhouse gas emissions, global warming has been accelerating. Aiming at reducing the amount of greenhouse gas in the atmosphere, this paper takes the dominant tree species in Gansu Province of China as the research objects, obtains the data on forests area through remote sensing and GIS technology, and evaluates the carbon sink capacity of forest trees. The research simulates the actual growth of the tree by computer, and compares cumulative carbon sinks between the control group and the experimental group, in order to determine the optimal harvesting ages of the tree. The results show that the optimal harvesting ages of Pinus armandii, Quercus liaotungensis, and Betula platyphylla are 61a, 120a and 61a, respectively. Compared with the control group, the cumulative carbon sinks of Pinus armandii, Quercus liaotungensis and Betula platyphylla are higher by 11.57%, 17.77% and 96.43%, respectively, while those of Abies fabri and Cupressus funebris are lower by 39.74% and 56.49%. In addition, this paper provides a decision-making method for managing tree harvesting from the perspective of carbon sink, which promotes the carbon sink capacity of the tree in a certain number of years. The research in this paper is of great significance to remote sensing applications and scientific decision-making by forest operators.

Evaluation of Carbon Sink Capacity of Tree Species in Gansu Province based on Remote Sensing Data

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Papers

Model predictive control and its application in agriculture: A review

Automatic carbon dioxide enrichment strategies in the greenhouse: A review

Evaluation of Carbon Sink Capacity of Tree Species in Gansu Province based on Remote Sensing Data