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Erschienen in:
Introduction to Data Analytics, Big Data, and Machine Learning Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Predictive Algorithms for Smart Agriculture

verfasst von : Rashmi Sharma, Charu Pawar, Pranjali Sharma, Ashish Malik

Erschienen in: Data Analytics and Machine Learning

Verlag: Springer Nature Singapore

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Abstract

Recent innovations in agriculture have made it smarter, more intelligent, and précised. Due to the technological advancement paradigm shift of agriculture practices from traditional to wireless digital incorporation of IoT, AI/ML, and Sensor technologies. Machine learning is a critical technique in agriculture for ensuring food assurance and sustainability. The machine learning algorithm starts from scratch to the final step—The selection of Crop, Soil Preparation, Seed Selection, Seed sowing, Irrigation, Fertilizer/Manure Selection, Control of Pests/weeds/diseases, Crop Harvesting, and Crop distribution for sales. ML algorithm suggests the right step for high-yield crops and precision farming. This article discusses how predictive ML supervised classification algorithms—especially K-Nearest Neighbor (KNN) can be helpful in the selection of crops, fertilizer to be used, corrective measures for the precision yield, and irrigation needs by looking at different parameters like climatic conditions, soil type, and previous crops grown in the field. The accuracy of algorithms comes out to be more than 90% depending on some uncertainties in the collection of data from different sensors. This results in well-designed irrigation plans based on the specific field conditions and crop needs.

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Vorheriges Kapitel Building Predictive Models with Machine Learning
Nächstes Kapitel Stream Data Model and Architecture
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Metadaten
Titel
Predictive Algorithms for Smart Agriculture
verfasst von
Rashmi Sharma
Charu Pawar
Pranjali Sharma
Ashish Malik
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Data Analytics and Machine Learning

Print ISBN: 978-981-9704-47-7

Electronic ISBN: 978-981-9704-48-4

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-0448-4_4

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