Abstract

Convolutional neural networks have demonstrated state-of-the-art performance in image classification and various other computer vision tasks. Plant disease detection is an important area of deep learning which has been addressed by many recent methods. However, there is a dire need to optimize these solutions for resource-constrained portable devices such as smartphones. This is a challenging problem because deep learning models are resource extensive in nature. This paper proposes an efficient method to systematically classify plant disease symptoms using convolutional neural networks. These networks are memory efficient and when coupled with the proposed training configuration it enables rapid development of industrial applications by reducing the training times. Another critical problem arises with the improper distribution of samples among classes known as the class imbalance problem, which is addressed by employing a simple statistical methodology. Transfer learning is a known technique for training small datasets which transfers pre-trained weights learned on a large dataset. However, during transfer learning, negative transfer learning is a common problem. Therefore, a stepwise transfer learning approach is proposed which can help in fast convergence, while reducing overfitting and preventing negative transfer learning during knowledge transfer across domains. The system is trained and evaluated on two plant disease datasets i.e., PlantVillage (a publicly available dataset) and pepper disease dataset provided by the National Institute of Horticultural and Herbal Science, Republic of Korea. The pepper dataset is particularly challenging as it contains images from different parts of the plant such as the leaf, pulp, and stem. The proposed system has dominated the previous works on the PlantVillage dataset and achieved 99% and 99.69% accuracy on the Pepper dataset and PlantVillage datasets, respectively.

Highlights

  • Agricultural yield is vulnerable to various biotic stresses which incur significant losses in terms of reduced production

  • The dataset is first analyzed for class imbalances which are known to be a deterrent in achieving good classification results

  • The stepwise transfer learning has helped in reducing the convergence time of Convolutional Neural Networks (CNN)

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Summary

Introduction

Agricultural yield is vulnerable to various biotic stresses which incur significant losses in terms of reduced production. In many under-developed countries, major crop yield is produced by small farmers for example, only in Africa, 80% of crops are produced by small farmers [2]. It is an alarming situation as lesser resources are available for smallholder farmers, which implies that the majority of agricultural production of under-developed countries is in danger. It is quite challenging due to the unavailability of expert opinion which usually delays the preliminary steps to mitigate the disease at earlier stages [3]. This delay is responsible for significant damage to the crops every year

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