Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things

Monteleone, Sergio; Moraes, Edmilson Alves de; Faria, Brenno Tondato de; Aquino, Plínio Thomaz; Maia, Rodrigo Filev; Neto, André Torre; Toscano, Attilio

doi:10.3390/s20247091

Cited by 44 publications

(33 citation statements)

References 115 publications

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“…Step 4. The score function is computed for interval-valued spherical fuzzy weights in Equation (12). 97…”

Section: Interval-valued Spherical Fuzzy Swaramentioning

confidence: 99%

“…11 Agriculture 4.0 issue has also appeared as the development of agriculture with the spread of the Internet of Things (IoT). 12 It has been claimed that Agriculture 4.0 age has come, which gets the form of smart agriculture with the deep combining of both traditional agriculture and modern information technology. 9 With the development of IoT, which allows all kinds of objects and devices to be connected wirelessly, many different data like soil conditions, meteorological information, land uses, and marketing demands can be produced in real-time in agriculture, and thus a digital process-based system can be established that includes Information and Communication Technology in machinery and equipment; and improves decision-making.…”

Section: Introductionmentioning

confidence: 99%

“…14 It can be said that the adoption of Agriculture 4.0 technologies is slower than expected. 12 The papers conducted so far on Agriculture 4.0 technologies have focused on technical aspects to improve agricultural practices and productivity. 23 However, due to the generally conservative nature of farmers, it is a challenging task that requires attention to ensure the adoption of Agriculture 4.0 technologies.…”

Section: Introductionmentioning

confidence: 99%

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Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach

Erdoğan

2021

Int J of Intelligent Sys

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Agriculture 4.0 is the usage of advanced technologies for ensuring sustainability and efficient use of resources in agriculture, which can be accepted one of the biggest challenges of mankind. With Agriculture 4.0 technologies, it is aimed to raise productivity, reduce waste and costs. To ensure that agriculture 4.0 technologies are adopted by farmers as soon as possible, the perspective and perception of farmers to these technologies should be analyzed firstly. For this purpose, we propose a decision-making framework to measure farmers' view of Agriculture 4.0 technologies and to perform a prioritization study based on the perception of use among these technologies. Multicriteria decision analysis is also utilized to deal with all qualitative and quantitative factors in the decision process. Within the scope of this study, interval-valued spherical fuzzy numbers are used to model the vagueness in the process in the best manner and to be able to reflect the uncertainty arising from the usage of linguistic variables in the decision process. The SWARA and MAIRCA multicriteria decision making (MCDM) methods, which have been used frequently in the literature and are applied very successfully in MCDM problems, have been firstly extended by spherical fuzzy sets (SFSs) and the advantages for these methods have been utilized within the framework of

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“…Step 4. The score function is computed for interval-valued spherical fuzzy weights in Equation (12). 97…”

Section: Interval-valued Spherical Fuzzy Swaramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach

Erdoğan

2021

Int J of Intelligent Sys

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“…However, on the one hand, sprinkler technology is a complex dynamic system, the operation of which is influenced by many stochastic factors. On the other hand, the object of the impact of technology -the field is a complex biological system with Spatio-temporal heterogeneity of the characteristics of soil, climate, plants, and, therefore, uneven need for irrigation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

et al. 2022

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The article proposes a methodology for optimizing the process of irrigation of crops using a phytoindication system based on computer vision methods. We have proposed an algorithm and developed a system for obtaining a map of irrigation for maize in low latency mode. The system can be installed on a center pivot irrigation and consists of 8 IP cameras connected to a DVR connected to a laptop. The algorithm consists of three stages. Image preprocessing stage -applying an integrated excess green and excess red difference (ExGR) index. The classification stage is the application of the method that we choose depending on the operating conditions of the system. At the final stage, a neural network trained using the Resilient Propagation method is used, which determines the rate of watering of plants in the current sector of the location of the sprinkler. The selected methods of pretreatment and classification made it possible to achieve an accuracy of plant identification up to 93%, growth stages -up to 92% (with unconsolidated maize sowing and good lighting). System performance up to 100 plants in one second, which exceeds the performance of similar systems. The neural network showed an accuracy of 92% on the training set and 87% on the test set. Dynamic analysis of spatial and temporal variability leads to an increase in productivity and efficiency of water use. In addition, given the ubiquitous distribution of agribusiness management systems, this approach is quite simple to implement in the farm's conditions.

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“…This will help-early disease detection, which in agriculture is a great challenge. IoT sensor nodes play a significant role in collecting real-time information play a significant role in collecting real-time information [1], [2]. These nodes can make the system highly practical by gathering data from crop fields in real-time to specify the agriculture system precisely.…”

Section: Introductionmentioning

confidence: 99%

Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things

Balram¹,

Kumar²

2022

IJACSA

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The Internet of Things can be defined as the network of physical objects that have sensors, software, and other technologies built into them in order to communicate and exchange data with other systems and devices over the internet. In intelligent agricultural advancements to increase the quality of agriculture, the Internet of Things (IoT) can be used. The manual monitoring of plant diseases is quite challenging. It demands enormous effort, expertise in the diseases of plants and the considerable time required for processing. The idea of automation in Smart Agriculture is implemented using the Internet of Things (IoT). They help monitor the plant leaf conditions, control water irrigation, gather images using installed IoT system which includes NodeMCU, cameras, soil moisture, temperature sensors and detect diseases in plants on the datasets collected from leaves. To detect plant diseases, image processing is applied. The detection of diseases comprises the acquisition of images, image pre-processing, segmenting an image, extracting and classifying features. In addition, the performance of two machine-learning techniques, such as a linear and polynomial kernel multi hidden extreme machine (MELM) and a support vector machine (SVM), has been studied. This paper discussed how plant diseases could be detected via images of their leaves. This analysis seeks to validate a proposed system for an appropriate solution to the IoT-based environmental surveillance, water irrigation system management and an efficient approach for leaf disease detection on plants. The proposed multi hidden layers extreme machine classification delivers good performance of 99.12% in the classification of leaf diseases in comparison to the Support Vector Machine classification, which gives 98%.

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Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things

Cited by 44 publications

References 115 publications

Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach

Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things

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