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

Escamilla-García, Axel; Soto-Zarazúa, Genaro M.; Toledano‐Ayala, Manuel; Rivas-Araiza, Edgar A.; Gastélum-Barrios, Abraham

doi:10.3390/app10113835

Cited by 113 publications

(64 citation statements)

References 209 publications

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“…Those techniques are largely used in the literature in the greenhouse microclimate context [69] such as to determine the indoor temperature and humidity [70], to predict inside climatic conditions [71] or to forecast energy consumption [72]. In [73], the authors review the applications of ANN in greenhouse technology.…”

Section: Traditional Pid/relay Control Methodsmentioning

confidence: 99%

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

et al. 2020

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Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use.

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Section: Traditional Pid/relay Control Methodsmentioning

confidence: 99%

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

et al. 2020

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“…An example is the greenhouse microclimate, which is complex, multiparametric, non-linear and depends on a set of external and internal factors. Artificial neural networks have been used to reduce energy consumption [17]. Other artificial intelligence (AI) applications are related, for example, to the detection of diseases in crops, to distinguish plant or flower types [18], optimization for UAVs [16], and the detection of insects [19].…”

Section: Artificial Intelligence/data Driven Decisionsmentioning

confidence: 99%

“…On the right side, different approaches based on IoT solutions through digitalization of a "thing" [32] are shown. Each of these solutions can generate a huge amount of data, called big data [33,34], which is collected in real-time and processed by optimization AI-based algorithms for data driven decisions and other technologies [16,17], aiming at monitoring, measuring, tracking or controlling, allowing to improve production results [10,11,13,15,18,19,24,25,[35][36][37][38][39][40][41][42][43][44]. Block chain is an emerging technology and is con-sidered a solution for the cybersecurity of IoT devices [22].…”

Section: Iot In Agriculturementioning

confidence: 99%

Development of Technological Capabilities through the Internet of Things (IoT): Survey of Opportunities and Barriers for IoT Implementation in Portugal’s Agro-Industry

et al. 2021

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The agro-industrial sector consumes a significant amount of natural resources for farming and meat production. By 2050, population growth is expected, generating more demand and, consequently, more consumption of scarce resources. This challenging scenario is a concern of the European Commission, revealed in the Green Deal commitment and by the United Nations’ 12th goal of sustainable development. Thus, organizations must increase productivity and be more sustainable as soon as possible. Internet of Things (IoT) is introduced as a solution to facilitate agro-food companies to be more eco-efficient, mainly facing difficulties on farms, such as food loss and waste, best efficiency in management of resources, and production. The deployment of this technology depends on the stage of maturity and potential of implementation. To assess and characterize companies, with respect of IoT implementation, a survey was applied in 21 micro, small and medium agro-food companies, belonging to milk, honey, olive oil, jams, fruticulture, bakery and pastry, meat, coffee, and wine sectors, in the central region of Portugal. As results, this paper reveals the stage of maturity, level of sophistication, potential, opportunities, solutions, and barriers for implementation of IoT. Additionally, suggestions and recommendations to improve practices are discussed.

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“…Employing the aspect of machine learning, the paper [14] presents the idea of environmental monitoring and disease detection in green house for Bangladesh. The paper [15] reviews the applications of artificial neural networks (ANNs) in greenhouse technology and presents model development in adaptation of new technologies. The author in [16] presents a method for early detection of leaf diseases in plants based on feature extraction.…”

Section: Review Of Literaturementioning

confidence: 99%

IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

Fatima¹,

Siddiqui²,

Ahmad³

2021

IJACSA

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Rapid industrialization and urbanization has led to decrease in agricultural land and productivity worldwide. This is combined with increasing demand of chemical free organic vegetables by the educated urban households, and thus, greenhouses are quickly catching trend for their specialized advantages especially in extreme weather countries. They provide an ideal environment for longer and efficient growing seasons and ensure profitable harvests. The present paper designs and demonstrates a comprehensive IoT based Smart Greenhouse system that implements a novel combination of monitoring, alerting, cloud storage, automation and disease prediction, viz. a readily deployable complete package. It continuously keeps track of ambient conditions like temperature, humidity and soil moisture conditions to ensure a higher yield of crop and immediate redressal in case of abnormal conditions. It also has a built-in automatic irrigation management system. Finally, it employs the most efficient deep learning model for disease identification with leaf images. Furthermore, with memory and storage optimization through cloud storage, an individual living in the city can also build a greenhouse and can monitor it from his home and take redressal methods as and when desired.

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

Cited by 113 publications

References 209 publications

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

Development of Technological Capabilities through the Internet of Things (IoT): Survey of Opportunities and Barriers for IoT Implementation in Portugal’s Agro-Industry

IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

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