A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Muhammad Shoaib; Babar Shah; Tariq Hussain; Akhtar Ali; Asad Ullah; Fayadh Alenezi; Tsanko Gechev; Farman Ali; Ikram Syed

doi:10.3389/fpls.2022.1095547

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Front Plant Sci. 2022 Dec 15:13:1095547. doi: 10.3389/fpls.2022.1095547. eCollection 2022.

Authors

Muhammad Shoaib¹, Babar Shah², Tariq Hussain³, Akhtar Ali⁴, Asad Ullah⁵, Fayadh Alenezi⁶, Tsanko Gechev^{4

7}, Farman Ali⁸, Ikram Syed⁹

Affiliations

¹ Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan.
² College of Technological Innovation, Zayed University, Dubai, United Arab Emirates.
³ High Performance Computing and Networking Institute, National Research Council (ICAR-CNR), Naples, Italy.
⁴ Department of Molecular Stress Physiology, Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria.
⁵ Department of Computer Science and Information Technology, Sarhad University of Science & Information Technology, Peshawar, Pakistan.
⁶ Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia.
⁷ Department of Plant Physiology and Molecular Biology, University of Plovdiv, Plovdiv, Bulgaria.
⁸ Department of Software, Sejong University, Seoul, Republic of Korea.
⁹ School of Computing, Gachon University, Seongnam-si, Republic of Korea.

Abstract

Plants are the primary source of food for world's population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant's survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

Keywords: CANet CNN; classification and DICE coefficient; disease detection; machine learning; plant lesion.