A Novel Model for Optimization of Resource Utilization in Smart Agriculture System Using IoT (SMAIoT)

Jani, Keyurbhai A.; Chaubey, Nirbhay

doi:10.1109/jiot.2021.3128161

Cited by 30 publications

(13 citation statements)

References 14 publications

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“…The second microcontroller, Esp 8266 Nodemcu v3 (number 9), is an electronic board module that operates as a WiFi connection and is based on the Esp 8266 chip [39]. This microprocessor manages data requests from the first microcontroller, as well as data transmission to subsystems 2 and 3.…”

Section: A Multiple Sensors Data Acquisition Subsystemmentioning

confidence: 99%

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The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

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Section: A Multiple Sensors Data Acquisition Subsystemmentioning

confidence: 99%

“…2 in number 2. This subsystem consists of two major components: data acquisition to the MySQL database webserver (number 14) as a place to store sensor data [39]. The second section of the website for local soil PH factor monitoring (number 15).…”

Section: A Multiple Sensors Data Acquisition Subsystemmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In 2022, Jani and Chaubey [6] have proposed a smart framework for agriculture that used the IoT to manage many kinds of inexpensive IoT sensors. They gather data from insects, water, air, and soil.…”

Section: A Related Workmentioning

confidence: 99%

“…Moreover, its complexity rate is higher and also it requires overcoming the convergence issues in the system. IoT [6] initialization cost is lower than other techniques and also it automatically identifies the pests, fertilizers, and pesticide sprays. Yet, it needs to reduce the human interaction and also it needs to provide higher privacy and security rate.…”

Section: B Problem Statementmentioning

confidence: 99%

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Xu,

Lakshmi Patibandla,

Arora

et al. 2024

IEEE Access

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Rich natural resources such as fertilizers, environment, groundwater, rivers, and land are abundant in many countries. Agriculture is the primary source of income for the people living in different countries. There have not been shortages of resources like river water and groundwater, in recent decades. But, the lack of knowledge on how to use those valuable resources is the main reason for resource wastage. The amount of water applied to crop fields in a variety of soil, weather, and crop growth stages can be managed and optimized using smart farming. The crop field's soil moisture can be measured using sensors positioned at various observation points, which will show how much water has been retained. Unfortunately, the smart farming system is not capable to receive the soil moisture data provided by the irrigation management due to issues with connectivity or sensor failure. Innovative agricultural approaches can be facilitated by the Internet of Things (IoT) technologies. These IoT nodes have encountered energy limitations and challenging routing techniques as a result of their low capacity. Therefore, it is imperative to resolve the issues by implementing an effective IoTbased irrigation system in the agricultural area. The major steps of the developed model are data collection and prediction. Initially, essential image and sensor data is attained from the benchmark resources. Next, the collected images are provided to the level of the irrigation prediction phase. This phase facilitates the farmers to maximize the crop yields and minimize the production cost. Here, effective irrigation prediction is performed using an Adaptive Hybrid (1D-2D) Convolution-based ShuffleNetV2 model (AHC-ShuffleNetV2). Moreover, the parameters of the suggested AHC-ShuffleNetV2 are optimized using a Fitness-based Piranha Foraging Optimization Algorithm (FPFOA). This increases the performance rates of the proposed model. Later, several experimental analyses are executed in the developed model over classical techniques to display their effectualness rate. When considering the sigmoid activation function, the implemented smart irrigation level prediction framework's RMSE was minimized by 73.15% of POA-ShuffleNetV2, 72.36% of RSA-ShuffleNetV2, 78.94% of MRS-ShuffleNetV2, and 79.47% of PFOA-ShuffleNetV2 respectively. Hence, it is revealed that the designed smart irrigation level prediction model attained low error rates and also achieved higher efficacy than the other baseline techniques.INDEX TERMS Irrigation level prediction, agricultural fields, smart internet of things, fitness-based piranha foraging optimization algorithm, adaptive hybrid (1D-2D) convolution-based shufflenetV2 model.

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“…Enhancing the information processing by automatically producing test cases and error reports using data from prior occurrences increases the efficiency of the operation [21]. This makes it possible to acquire an approximation of the solutions and makes it easier to choose choices that are either error-free or may result in fewer mistakes [22]- [23]. This makes it possible to get an estimate of the solutions.…”

Section: Introductionmentioning

confidence: 99%

Design of an efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets

Bhoyar

Kanojia

Chourasia

et al. 2023

tjes

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Design of smart irrigation systems is a transdisciplinary work involving selecting optimal field-specific sensing devices, data capturing from these devices, their pre-processing, feature analysis, classification, IoT (Internet of Things) based actuation, and feedback operations. A wide variety of smart irrigation models, including IBM ThingSpeak, Tracxn, Farm Connect, Hydrawise, etc. have been proposed by researchers, and each of them have its own operational & deployment-specific characteristics. These models are either highly complex or have slower response time & low efficiency when applied to real-time irrigation scenarios. Moreover, these models have a higher cost, which limits their scalability levels. To overcome these issues, this text discusses design of a novel efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets. The proposed platform uses low-cost components for sensing moisture levels, temperature levels, rain probability, NPK (Nitrogen, Phosphorous, and Potassium) levels, and soil types. These sensed values are processed via a Grey Wolf Optimizer (GWO) to identify optimum sensor types, and then a fuzzy decision layer is used for irrigation. This layer assists in the identification of efficient water flow for different crop types. The resulting growth of plants is fed-back into the model, and a Genetic Algorithm (GA) is applied to tune the fuzzy rules for better water-flow under multiple types of crops. Due to the integration of GWO with Fuzzy Logic controller, the proposed model improved yield efficiency by 8.5%, reduced computational delay by 4.9%, and reduced deployment cost compared to standard smart irrigation models. Due to these advantages, the proposed model is capable of deployment for a wide variety of real-time smart irrigation use cases.

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A Novel Model for Optimization of Resource Utilization in Smart Agriculture System Using IoT (SMAIoT)

Cited by 30 publications

References 14 publications

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Design of an efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets

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