A survey on intelligent agents and multi-agents for irrigation scheduling

Jiménez, Andrés-F; Cárdenas, Pedro F.; Canales, Antonio Ruíz; Jiménez, Fabián; Portacio, Alfonso A.

doi:10.1016/j.compag.2020.105474

Cited by 42 publications

(18 citation statements)

References 202 publications

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“…The output gate o t uses the sigmoid function given by Equation (6) to decide which information in the cell is used to calculate the output of the LSTM unit. The block input z t creates a new vector that could be added to the new state using the tanh function given by Equation (7). The memory cell c t decides whether to update the information obtained from the previous inputs and the current information provided by Equation (8).…”

Section: Lstm Architecturesmentioning

confidence: 99%

Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

2021

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In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.

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Section: Lstm Architecturesmentioning

confidence: 99%

Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

2021

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“…Artificial Intelligence has shown better results than conventional approaches in several of projects; these works involve intelligent agents that perform several tasks in pursuit of optimum results. It was shown in (Jimenez et al, 2020;Salazar et al, 2013), that an intelligent agent's capable of considering timing, amount of water, and properly implementing them in what they detail as Spatio-temporal variations of the soil-plant-atmosphere system, gathering impressive results in terms of water efficiency and precision irrigation. As agents work with microcontrollers like Arduino, they may create further applications.…”

Section: Artificial Intelligence With Microcontrollersmentioning

confidence: 99%

Artificial intelligence-controlled pole balancing using an Arduino board

Orellana

Chang

2021

rte

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Automation Process (AP) is an important issue in the current digitized world and, in general, represents an increase in the quality of productivity when compared with manual control. Balance is a natural human capacity as it relates to complex operations and intelligence. Balance Control presents an extra challenge in automation processes, due to the many variables that may be involved. This work presents a physical balancing pole where a Reinforcement Learning (RL) agent can explore the environment, sense its position through accelerometers, and wirelessly communicate and eventually learns by itself how to keep the pole balanced under noise disturbance. The agent uses RL principles to explore and learn new positions and corrections that lead toward more significant rewards in terms of pole equilibrium. By using a Q-matrix, the agent explores future conditions and acquires policy information that makes it possible to maintain stability. An Arduino microcontroller processes all training and testing. With the help of sensors, servo motors, wireless communications, and artificial intelligence, components merge into a system that consistently recovers equilibrium under random position changes. The obtained results prove that through RL, an agent can learn by itself to use generic sensors, actuators and solve balancing problems even under the limitations that a microcontroller presents.

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“…Some previous review works have investigated the current trend in the area of smart monitoring and control of irrigation [6,8,[21][22][23]. Numerous papers have explored the role of machine learning in enabling smart irrigation [14,[24][25][26][27][28][29][30]; these are summarized in Table 1. The majority of these existing works have focused on the application of supervised and unsupervised learning for smart irrigation systems.…”

Section: Introductionmentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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A survey on intelligent agents and multi-agents for irrigation scheduling

Cited by 42 publications

References 202 publications

Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

Artificial intelligence-controlled pole balancing using an Arduino board

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

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