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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

IoT Enabled Crop Prediction and Irrigation Automation System Using Machine Learning

Author(s): Raj Kumar* and Vivek Singhal

Volume 15, Issue 1, 2022

Published on: 04 September, 2020

Page: [88 - 97] Pages: 10

DOI: 10.2174/2666255813999200904132431

Price: $65

Abstract

Aim: India, an agricultural country with more than 50% population, depends on agriculture as the main source of income. Increasing population, shrinking agricultural land and fragmentation may create severe challenges to food safety in India. Therefore, there is an intense need for the application of technological innovations in the agriculture sector to improve its growth.

Objectives: The objective of the study is to design and develop an automated crop yield maximization system with an irrigation automation process using the Internet of Things (IoT) and machine learning. The proposed study focuses on the maximization of crop yield and profits of the farmer using crop prediction and automation of the irrigation process leading to water conservation.

Methods: The proposed system firstly rolls around, making the irrigation system automated to ease the burden of getting water for the plants when they need it. In an automatic irrigation system, data sensed by the temperature sensor and soil sensor in real-time are sent to the microcontroller board, and based on the values received, it will instruct the relay to turn on/off the water pump and thus automate the irrigation process. Secondly, it involves predicting suitable crop types using soil parameters and weather conditions that may be implanted by the farmers, using machine learning techniques, displayed on an android based application.

Results: The system proposed in this work recommends suitable crops as per the current weather and soil conditions in a particular geographical area by applying the XGBoost classifier with the automation of the irrigation process. The study also does a comparative performance analysis of the XGBoost classifier based on the F1 Score and Accuracy.

Conclusion: The study proposed a micro-controller-based model for crop maximization and irrigation automation. The system mainly fulfills two major objectives of agriculture-maximization of crop yield and farmer's profit using crop prediction, and automation of the irrigation process leads to water conservation.

Keywords: IoT, crop yield, irrigation, automation, maximization, machine learning.

Graphical Abstract

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