Article
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Smart Agriculture: IoT and Deep Learning for Precision Crop Management
Version 1
: Received: 11 August 2024 / Approved: 11 August 2024 / Online: 13 August 2024 (03:18:08 CEST)
How to cite: Najeeb, H.; Naseer, A.; Tamoor, M. Smart Agriculture: IoT and Deep Learning for Precision Crop Management. Preprints 2024, 2024080747. https://doi.org/10.20944/preprints202408.0747.v1 Najeeb, H.; Naseer, A.; Tamoor, M. Smart Agriculture: IoT and Deep Learning for Precision Crop Management. Preprints 2024, 2024080747. https://doi.org/10.20944/preprints202408.0747.v1
Abstract
Pakistan is an agricultural country, and an exporter of crops in many countries. The crop production is an important factor to earn revenue for the country. But the agriculture of Pakistan is still not revolutionized and no modern system has been deployed commonly. Whereas the farmers face difficulty in growing some crops because those crops needs more care and calculated steps. Moreover the farmers face difficulty at different stages of the crop growth like to check fertility of soil and if the soil is non-fertile then which chemical property is lacking. The objective of this research is to provide digitize the agriculture of Pakistan. An emerging technology IoT and Deep learning based system will be developed. The IoT part contains some devices like sensors,gateways and communication technologies whereas the software part consists of Deep learning models that can make predictions about the fertility of the soil. The motive of this system is to provide best results in term of accuracy. Whereas the dataset used is publicly available on kaggle but it was of non-fertile values, so data augmentation is also performed to generate data for fertile values. We have implemented LSTM,RNN,CNN among them LSTM performed best for the prediction of soil fertility.
Keywords
Internet of things; Smart Agriculture; Deep Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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