Pseudoinverse learning autoencoder with DCGAN for plant diseases classification
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Pseudoinverse learning autoencoder with DCGAN for plant diseases classification Mohammed A. B. Mahmoud1
· Ping Guo2 · Ke Wang3
Received: 3 September 2019 / Revised: 10 June 2020 / Accepted: 24 June 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Pest infestation of crops and plants impacts agricultural development. Generally, farmers or specialist observe the plants with the naked eye to recognise and diagnose ailments. However, this technique can be time-consuming, costly and inexact. In contrast, auto-detection using image processing methods gives fast and precise results. This paper introduces a new plant disease identification model predicated on leaf image classification that employs a deep convolutional generative adversarial network (DCGAN) along with a classifier identified by multilayer perceptron (MLP) neural networks trained with a pseudoinverse learning autoencoder (PILAE) algorithm. The DCGAN performes two tasks: (1) synthesis of the minor class images to overcome the issue of imbalance in the dataset and (2) extracting deep features of all images within the dataset. The PILAE training procedure is not required to identify the learning control variables or indicate the number of hidden layers. Consequently, the PILAE classifier can fulfil exceptional execution with regard to training efficiency and reliability. Empirical results from PlantVillage dataset possess demonstrated how the presented method yields positive results with other models and reasonably minimal complexly. Keywords Plant diseases · DCGAN · Pseudoinverse learning
1 Introduction Plant diseases can cause substantial damage to agriculture plants by significantly reducing yield [27]. Pests are a typical exemplary of disease that may severely impact early plant This work is fully supported by the grants from the National Natural Science Foundation of China (NSFC) (61375045), and the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the NSFC and Chinese Academy of Sciences (CAS). Mohammed A. B. Mahmoud
[email protected] 1
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
2
School of Systems Science, Beijing Normal University, Beijing, China
3
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
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development [9]. Similarly, in humid weather, pests can cause very harmful disease in more mature plants and can impact plant leaves, fruit and stems [9]. Protecting plants from ailments is key to guarantee the quality and quantity of crops [11]. An effective protection approach should focus on early recognition of the condition to enable the choice of ideal treatment at the proper time to prevent it from spreading [2]. Generally, this detection can be achieved by professionals with academics knowledge strengthened by working experience on symptoms of and factors behind diseases [9]. Moreover, those professionals must monitor crops consiste
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