Open Access

ARTICLE

An Ingenious IoT Based Crop Prediction System Using ML and EL

Shabana Ramzan¹, Yazeed Yasin Ghadi², Hanan Aljuaid³, Aqsa Mahmood^1,*, Basharat Ali⁴

1 Department of Computer Science & IT, Government Sadiq College Women University, Bahawalpur, 63100, Pakistan
2 Department of Computer Science/Software Engineering, Al Ain University, Al Ain, 64141, UAE
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), Riyadh, 11671, Saudi Arabia
4 Agronomic Research Station, Bahawalpur, 63100, Pakistan

* Corresponding Author: Aqsa Mahmood. Email:

(This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)

Computers, Materials & Continua 2024, 79(1), 183-199. https://doi.org/10.32604/cmc.2024.047603

Received 10 November 2023; Accepted 20 December 2023; Issue published 25 April 2024

Abstract

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggesting a suitable crop to cope the food scarcity. This paper proposes a system comprised of two modules, first module uses static data and the second module takes hybrid data collection (IoT-based real-time data and manual data) with machine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield. Python is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solution for the farmers to process the data and predict the suitable crop. We implemented the proposed system in the field. The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

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Keywords

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