Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop

Hamna Waheed, Noureen Zafar, Waseem Akram, Awais Manzoor, Abdullah Gani and Saif ul Islam
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Hamna Waheed: Department of Computer Science, Pir Mehr Ali Shah Arid Agriculture University-PMAS AAUR, Rawalpindi 46000, Pakistan
Noureen Zafar: Department of Computer Science, Pir Mehr Ali Shah Arid Agriculture University-PMAS AAUR, Rawalpindi 46000, Pakistan
Waseem Akram: Department of Informatics, Modeling, Electronics, and Systems (DIMES), University of Calabria, 87036 Rende, Italy
Awais Manzoor: Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
Abdullah Gani: Faculty of Computing and Informatics, University Malaysia Sabah, Labuan 88400, Malaysia
Saif ul Islam: Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan

Agriculture, 2022, vol. 12, issue 6, 1-17

Abstract: Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99 % for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96 % . For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy ( 96 % ). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.

Keywords: deep learning; plant diseases detection; pest pattern; agricultural diagnostic system (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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