Towards Smart Farming: Fog-enabled intelligent irrigation system using deep neural networks

Cordeiro, Matheus G.; Markert, Catherine; Araújo, Sayonara S.; Campos, Nídia G. S.; Gondim, Rubens Sonsol; Silva, Ticiana L. Coelho da; Rocha, Atslands Rego da

doi:10.1016/j.future.2021.11.013

Cited by 48 publications

(21 citation statements)

References 19 publications

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“…A smart agriculture solution provides the agricultural sector with new levels of control and automated decision-making, allowing for a coherent ecosystem. With the current pace of development of smart agriculture, it is now feasible to construct a farm-wide sensor network ( Chlingaryan et al., 2018 ; Herman et al., 2019 ; Akhter and Sofi, 2022 ; How to grow tomatoes indoors, 2022 ; Cordeiro et al., 2022 ) that allows for practically continuous round-the-clock monitoring of a farm. Consequently, theoretical and practical frameworks have been developed to link the state of crops, soil, and farm animals with production inputs, such as water, fertilizer, pesticides, and plant medications ( Mehra et al., 2018 ; Kashyap et al., 2018 ; Herman et al., 2019 ; Parihar, 2019 ; Junior et al., 2022 ).…”

Section: Why Smart Agriculture?mentioning

confidence: 99%

“…As of now, many research studies in the field of smart agriculture have proposed different novel solutions and methodologies to address various research problems, including the prediction of daily climate for the next crop cycle and the amount of harvest in the next growth cycle ( Chlingaryan et al., 2018 ; How to grow tomatoes indoors, 2022 ; Akhterand and Sofi,2021; Herman et al., 2019 ; Cordeiro et al., 2022 ). Various smart agriculture startup companies are established worldwide to reach out to more farmers and widen the market.…”

Section: Introductionmentioning

confidence: 99%

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Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

Thilakarathne

Bakar²,

Abas³

et al. 2023

Front. Plant Sci.

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Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21st century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment.

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Section: Why Smart Agriculture?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

Thilakarathne

Bakar²,

Abas³

et al. 2023

Front. Plant Sci.

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show abstract

“…In this context, the recursive adaptive filter method was employed to remove the noise, handle the missing values, and normalize the data [77]. Cordeiro et al [78] developed a deep learning model that was capable to anticipate the soil moisture availability in the agricultural land and addressing the sensor missing data and failure ambiguities. Among the studied algorithms, the k-nearest neighbors (KNN) algorithm bypasses the problems and accurately predicts the irrigation water need.…”

Section: Micro Controllermentioning

confidence: 99%

Agrovoltaic and Smart Irrigation: Pakistan Perspective

Asfahan¹,

Ahmad²,

Majeed³

et al. 2023

Irrigation and Drainage - Recent Advances

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The present study aims to investigate the prospects and challenges that need to be encountered for the adaptation of the novel agrovoltaic irrigation system (AVIS) in Pakistan. The agro-production scenario in Pakistan is periodically declining and leading toward the high delta crops, which develop severe pressure on the conventional energy and water resources. Groundwater might be a viable water source, but its pumping requires massive energy. In addition, excessive pumping declines the water table at a higher pace as compared to the recharge rate hence leading the country toward the exploitation of the valuable reservoir. The AVIS could be an energy-efficient and reliable irrigation solution in a manner of harvesting solar energy for driving smart irrigation systems capable to pumps the metered groundwater according to field requirements. Lack of local understanding, skilled/technical personnel, dependence on local technology, and major capital expenditures might impede technological adaption. The government should take necessary measures to replenish the groundwater reservoirs and also execute research projects that strengthen ground knowledge of AVIS.

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“…Cordeiro et al predicted soil moisture for irrigation management as soil moisture data was not properly retrieved from the farm due to sensor failure. A fog-enabled smart system for irrigation was deployed using neural networks [62][63][64][65][66].…”

Section: Artificial Neural Network and Machine Learning For Irrigationmentioning

confidence: 99%

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

Veerachamy

Ramalakshmi

Kavin

et al. 2022

Water

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The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications.

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Towards Smart Farming: Fog-enabled intelligent irrigation system using deep neural networks

Cited by 48 publications

References 19 publications

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

Agrovoltaic and Smart Irrigation: Pakistan Perspective

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

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