Wheat disease detection for yield management using IoT and deep learning techniques

Sana Akbar; Khawaja Tehseen Ahmad; Muhammad Kamran Abid; Naeem Aslam

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Wheat disease detection for yield management using IoT and deep learning techniques

Author(s)

Sana Akbar Department of computer science, university of NFC IET, Multan, Pakistan

Khawaja Tehseen Ahmad Department of computer science, Bahauddin Zakariya University, Multan 60800, Pakistan

Muhammad Kamran Abid Department of computer science, university of NFC IET, Multan, Pakistan

Naeem Aslam Department of computer science, university of NFC IET, Multan, Pakistan

Abstract

Our economy is mostly based on agriculture. One of the difficult problems in the agriculture sector is crop yield predictions. Crop yield prediction using a machine learning algorithm with the help of IoT increases the production of wheat yield and improves the quality of yield. Today's low agricultural production is a problem for farmers. Low crop output is mainly caused by a lack of information regarding soil fertility and crop selection, and proper crop selection is the key to maximizing crop yield. One of the interesting agricultural research areas where deep learning (DL) algorithm concepts can be used is the identification of wheat disease from images. We consider two leaf diseases septoria and stripe rust and also take a healthy leaf and then do a comparison between the leaves using CNN. As a contribution, we developed a system ML with a neural network mobilenet and efficient net-b3 that detects wheat leaf disease and improves accuracy gradually. Moreover, we do a complete review of yield management in which IoT sensors are used with machine learning algorithms. This study aims to create a system that can correctly choose a crop for maximum yield utilizing IoT devices and machine learning (ML) algorithms. We achieve 97% accuracy using mobilenet which is better than the efficient net. The presented work also applied different image augmentation techniques to remove the problem of overfitting. The presented work is compared with the state-of-the-art method in terms of accuracy and precision score.

Publication Details

Page(s) 80-89

DOI DOI not available

Published Journal: VFAST Transactions on Software Engineering, Volume: 10, Issue: 3, Year: 2022

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Keywords

machine learning, disease detection, CNN, MobileNet, efficient net-b3, crop yield prediction

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