Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique

Habib Khan, Ijaz Ul Haq, Muhammad Munsif, Mustaqeem, Shafi Ullah Khan and Mi Young Lee ()
Additional contact information
Habib Khan: Sejong University, Seoul 05006, Korea
Ijaz Ul Haq: Sejong University, Seoul 05006, Korea
Muhammad Munsif: Sejong University, Seoul 05006, Korea
Mustaqeem: Interaction Technology Laboratory, Department of Software Convergence, Sejong University, Seoul 05006, Korea
Shafi Ullah Khan: Department of Electronics, Islamia College University, Peshawar 25000, Pakistan
Mi Young Lee: Sejong University, Seoul 05006, Korea

Agriculture, 2022, vol. 12, issue 8, 1-20

Abstract: Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques.

Keywords: artificial intelligence; computer vision; machine learning; precision agriculture; wheat diseases (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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