Classification of Apple Disease Based on Non-Linear Deep Features

Applied Sciences

Ram Sarkar¹  Muhammad Ahmad²  Erick Rodríguez-Esparza³  Marco Pérez-Cisneros⁴  Diego Oliva⁴  Hamail Ayaz⁵ 

[1] Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India;Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan;DeustoTech, Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain;División de Electrónica y Computación, Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Guadalajara 44430, Mexico;Faculty of Engineering and Design and Centre for Precision Engineering, Materials and Manufacturing Research, Institute of Technology Sligo, F91 YW50 Sligo, Ireland;

关键词: apple diseases;    blotch;    scab;    rot;    classification;    deep learning;   

DOI  :  10.3390/app11146422

来源: DOAJ

【 摘 要 】

Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using deep generative images to obtain higher accuracy. In order to achieve this, our work progressively modifies a baseline model by using an end-to-end trained DCNN model that has fewer parameters, better recognition accuracy than existing models (i.e., ResNet, SqeezeNet, and MiniVGGNet). We have performed a comparative study with state-of-the-art CNN as well as conventional methods proposed in the literature, and comparative results confirm the superiority of our proposed model.

【 授权许可】

Unknown