Sensor Based Smart Agriculture with IoT Technologies: A Review

Pyingkodi, M.; Thenmozhi, K.; Nanthini, K.; Karthikeyan, Muthukumarasamy; Palarimath, Suresh; Erajavignesh, V.; Kumar, G. Bala Ajith

doi:10.1109/iccci54379.2022.9741001

Cited by 33 publications

(6 citation statements)

References 30 publications

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“…Sensors may also use to assess the needs of the soil based on the readings that the sensors collect, and actuators to provide the equivalent quantity of nutrients or water to the soil (Parashar et al 2020). The sensor is mostly used in agriculture to measure soil moisture levels, disease detection, and NPK (nitrogen, phosphorus, potassium) levels (Pyingkodi et al 2022). In order to provide information that enables farmers to monitor crops, maximize yields, and adapt to altering environmental conditions, precision farming uses a variety of sensing technologies, including:…”

Section: Commercially Available Sensors For Agricultural Securitymentioning

confidence: 99%

RETRACTED: Advances in nanosensor strategies for on-site detection of pesticide residues in agricultural products

Dixit,

Kumar,

Bajpai

et al. 2024

Preprint

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This review intends to integrate the relevant information related to different sensing methods for pesticide detection in agriculture industry. The use and/or misuse of industrial pesticides results in agricultural pesticide toxicity. Pesticides have been shown to have negative health and environmental consequences. Exponential advancement in nanotechnology has aided the transition of existing food and agriculture industries. The nanotechnological approaches such as remediation, detection, and pollution control have potential impact on the environment. Nanomaterials are gaining huge popularity in agriculture sector as nanosensing strategies for pesticide detection. Nanosensors possess several properties such as selectivity, robustness, and cost-effectiveness etc. Nanosensors developed for the real time monitoring of the pesticides present in the crops and food system, help in improving the crop productivity management as compared to other conventional sensing methods. Therefore, the present study reviews and briefly describes biosensing methods such as electrochemical, potentiometric, amperometric, calorimetric, optical biosensors, and immunosensors for pesticide detection in agriculture sector along with mechanisms and interactions of nanoparticles and enzyme-based sensors with pesticide components and their potential application as novel nanosensors for pesticide detection. The use of pesticides in agriculture sector, their toxicity, health effects, and the available specific detection techniques against them have also been discussed.

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Section: Commercially Available Sensors For Agricultural Securitymentioning

confidence: 99%

RETRACTED: Advances in nanosensor strategies for on-site detection of pesticide residues in agricultural products

Dixit,

Kumar,

Bajpai

et al. 2024

Preprint

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“…According to (9) this paper the accuracy of crop yield prediction is compared for each of the algorithms. With a 95 percent accuracy, the Random Forest method was shown to be the best for the provided dataset with 95%.…”

Section: Resultsmentioning

confidence: 99%

IoT Enabled Smart Agriculture using Digital Dashboard

Spandana¹,

Pabboju²

2023

IJST

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Objectives:To develop an IoT-based smart device to retrieve soil and water parameters by collecting and storing sensor-based data and to make crop recommendations. The primary goal of this work is to create a link between farmers and their farms. Methods: This work produces a smart device to predict suitable crop based on several parameters using a technical approach that makes use of the Internet of Things, Machine learning algorithms, and Sensor Networks. The Gaussian Naive Bayes algorithm is used in the Prediction module to predict suitable crop types. Several components are used to make an IoT system like Raspberry Pi, DHT11 sensor, soil moisture sensor, pH sensor, LDR sensor, Ultrasonic sensor, NPK sensor, and other components to collect data related to soil properties and weather conditions. Soil samples were collected from different fields and tested by switching the IoT kit and the message will be passed to the field owner. Dataset is used from the Kaggle database to train the model for crop prediction. Seven parameters have been taken in the trained dataset such as pH, N, P, K, temperature, humidity, and rainfall. All these values were collected through soil properties sensors and weather condition sensors. Existing traditional methods like soil testing laboratories involve lengthy and costly procedures for soil testing and crop prediction. The proposed system will produce results faster at a lesser cost while making digital data available on the cloud server. Findings: The proposed system performs efficiently when compared to the previous report with 99.3% accuracy over 97.18%. The existing approach for lab-based soil testing takes approximately 5 -8 days; whereas the proposed system using an IoT kit can perform n number of soil tests on a single device and the results can be viewed instantly. Novelty: Most of the existing systems like smart agriculture system and smart irrigation system use Arduino Uno for soil test which doesn't support high-end sensors and works only with desktop computer traditional programs. The existing system for nutrient identification uses a soil Ec sensor and color sensor to find N, P, and K values which include multi-steps and are time-consuming. The proposed system uses Raspberry Pi for soil tests which are more compatible with implementing the latest machine-level algorithms. In this system, the NPK sensor is used instead of soil Ec sensor and color sensor which will give the N, P, and K values directly.

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“…However, the independent features, labels and machine learning algorithm used for forecasting were not discussed. The authors in [24] discuss challenges that are experienced by farmers using IoT sensors such as security of the network where it is deployed, procurement of the sensors, and lack of broadband connectivity since the farms are in rural areas where network connectivity is very poor. No government regulations affect the use of sensors in agriculture and the adverse effect of using these sensors on crops or animals within a smart farm has been indicated.…”

Section: Technologies For Smart Farmingmentioning

confidence: 99%