IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

Bakthavatchalam, Kalaiselvi; Karthik, B.; Thiruvengadam, V. Sanjay; Muthal, Sriram; Jose, Deepa V.; Kotecha, Ketan; Vijayakumar, V.

doi:10.3390/technologies10010013

Cited by 61 publications

(48 citation statements)

References 29 publications

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“…A smart agriculture system using DL-based computer vision is promising due to the massive growth of agriculture data commonly collected from IoT sensors. Other researchers also studied and applied traditional machine learning (also in combination with DL) techniques, such as fuzzy logic, SVM, supervised learning, decision tree, linear regression, and KNN [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. The authors of [ 52 ] proposed a small-scale agriculture machine to irrigate and weed automatically in the cultivated area.…”

Section: Resultsmentioning

confidence: 99%

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Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Adli

Remli

Wong

et al. 2023

Sensors

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As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

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Section: Resultsmentioning

confidence: 99%

“…The SVM classifier is mainly used for various classifications in smart farming. In [ 55 ], the authors proposed an IoT framework using machine learning algorithms, as illustrated in Figure 4 . The primary purpose of the framework was to measure and predict crops using supervised learning.…”

Section: Resultsmentioning

confidence: 99%

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Adli

Remli

Wong

et al. 2023

Sensors

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“…WEKA tool is used for data analysis by ML algorithms. A decision table classifier and multilayer perceptron rule-based classifier JRip are used for classification [21].…”

Section: Related Workmentioning

confidence: 99%

Crop Yield Maximization Using an IoT-Based Smart Decision

et al. 2022

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Today, farmers are suffering from the low yield of crops. Though right crop selection is the main boosting key to maximize crop yield by doing soil analysis and considering metrological factors, the lack of knowledge about soil fertility and crop selection is the main reason for low crop production. In the changed current climate, the farmers having primitive knowledge about conventional farming are facing challenges about making sagacious decisions on crop selection. The selection of the same crop in every seasonal cycle makes the low soil fertility. This study is aimed at making an efficient and accurate system using IoT devices and machine learning (ML) algorithms that can correctly select a crop for maximal yield. Such a system is reliable as compared to the old laboratory testing manual systems, which bear the chances of human errors. Correct selection of a crop is predominantly a priority in agricultural arena. As a contribution, we propose an ML-based model, Smart Crop Selection (SCS), which is based on data of metrological and soil factors. These factors include nitrogen, phosphorus, potassium, CO2, pH, EC, temperature, humidity of soil, and rainfall. Existing IoT-based systems are not efficient as compared to our proposed model due to limited consideration of these factors. In the proposed model, real-time sensory data is sent to Firebase cloud for analysis. Its results are also visualized on the Android app. SCS ensembles the following five ML algorithms to increase performance and accuracy: Decision tree, SVM, KNN, Random Forest, and Gaussian Naïve Bayes. For rainfall prediction, a dataset containing historical data of the last fifteen years is acquired from Bahawalpur Agricultural Department. This dataset and an ML algorithm, Multiple Linear Regression leverages prediction of the rainfall in future, a much-desired information for the health of any crop. The Root Mean Square Error of the rain fall prediction model is 0.3%, which is quite promising. The SCS model is trained for 11 crops’ prediction, while its accuracy is 97% to 98%.

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“…Computational and Mathematical Methods in Medicine comprehensive ebb periods and raises the nutritional content of the things produced. The fundamental goal of this case study, according to Bakthavatchalam et al [30], is to evolve a model that forecasts extreme yield crops and precision farming. The projected method posing incorporates contemporary science, the Internet of Things, and farming's detracting measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

Cited by 61 publications

References 29 publications

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review

Crop Yield Maximization Using an IoT-Based Smart Decision

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

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