Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain

Colombo-Mendoza, Luis Omar; Paredes-Valverde, Mario Andrés; Salas-Zárate, María del Pilar; Valencia‐García, Rafael

doi:10.3390/app12041940

Cited by 23 publications

(7 citation statements)

References 25 publications

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“…Different sensor nodes, network layer protocols, cloud services and ML algorithms developed for smart agriculture applications viz. irrigation monitoring ( [1], [26], [27], [34], [43], [44]), production process management ( [28], [29], [41]), plant growth and disease monitoring ( [30], [31], [32], [38], [39], [42]) and precision agriculture ( [33], [34], [40]) are considered in Table III.…”

Section: Review Discussionmentioning

confidence: 99%

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Gupta

Malhotra

Kumar

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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The concept of "smart agriculture" relies on the integration of sensors within an Internet of Things (IoT) network. Machine learning (ML) algorithms are integrated at various levels of the IoT system design to augment its functionality and enhance its capability. This article is a bibliometric review of 42 articles published between 2018 and 2022 using the Web of Science database. The results of the review showed an exponential growth in the use of ML algorithms in IoT systems for different agriculture applications. Additionally, two key research questions are addressed in this article, one being the development of IoT-ML-enabled smart agriculture over the past five years, and the second being the main research gaps for applications of machine learning and IoT in smart agriculture. The article concludes with a discussion of the results and future directions for research in the field.

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Section: Review Discussionmentioning

confidence: 99%

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Gupta

Malhotra

Kumar

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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“…However, the popularity and communication dynamics of short videos are influenced by various factors, including user interests, content quality, social network effects, geographical location, and others. The intricate interplay of these factors poses a formidable challenge in accurately predicting the popularity of short videos [4][5][6]. Conventional approaches often hinge on uncomplicated metrics like user feedback, viewing frequencies, or likes; nevertheless, these metrics fall short of fully capturing the intricacies inherent in short video communication.…”

Section: A Research Background and Motivationsmentioning

confidence: 99%

The Short Video Popularity Prediction Using Internet of Things and Deep Learning

He,

2024

IEEE Access

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In order to furnish valuable insights and solutions applicable to content creators, social media platforms, academic research, and general users, this investigation integrates the Internet of Things (IoT) with deep learning regression models to examine methodologies for predicting the popularity of short videos. Within the context of cross-cultural communication, a proposed Content Popularity Rank Prediction based on the Convolutional Neural Network (CPRP-CNN) model relies exclusively on the personal attributes of the publisher and the textual characteristics of short videos to anticipate the viewership levels of short videos promptly following their release. Through simulated experiments, the model's performance is assessed, revealing that the utilization of the Rectified Linear Unit (Relu) activation function in the CPRP-CNN model enhances accuracy by 42.2% when contrasted with the use of the sigmoid function. This enhancement is coupled with a 37.8% reduction in cross-entropy loss. Furthermore, the proposed CPRP-CNN model attains a cross-entropy of 0.692 and an accuracy of 74.7%, exhibiting superior Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 2.728 and 1.751, respectively, when compared to alternative prediction models. These outcomes signify that the amalgamation of deep learning models with fused features within the IoT context significantly ameliorates the predictive efficacy of short video popularity. The research findings contribute to the enhancement of personalized and precise short video content recommendations.

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“…Colombo-Mendoza et al presented a design of smart farming system using IoT sensors for data collection and ML algorithms. A new data mining approach is used to combine two types of datasets: climate data and crop production data for crop yield prediction [19]. Khongdet et al proposed a model for smart crop tracking and monitoring by storing real-time data from IoT devices.…”

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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Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain

Cited by 23 publications

References 25 publications

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

The Short Video Popularity Prediction Using Internet of Things and Deep Learning

Crop Yield Maximization Using an IoT-Based Smart Decision

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