# Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning
## 1 Introduction  
The outbreak of plant diseases is a threat to food production and security at the global scale. It can cause disastrous consequences for smallholder farmers representing 85% of the world's farms whose livelihoods depend on healthy crops [1]. In order to manage the detection and spread of plant diseases, several diagnostic protocols are developed in literature. However, challenges exist that prevent this kind of technology from being adopted in practice [2]. In previous research, a variety of generic machine learning (ML) methods are popularity in plant diseases identification including K-nearest neighbor (KNN), support vector machines (SVM), artificial neural networks (ANN), amongst others [3]. These methods are relatively successful under limited and constrained setups. However, these traditional machine learning methods have the problems of incomplete feature selection and fussy manual feature selection [4]. Deep Learning (DL) in particular offers very novel approaches to classify images because it extends classical ML by adding more \"depth\" (complexity) into the model [5]. These complex models can increase classification accuracy or reduce generalization error. The agricultural field, and especially the image-based plant diseases identification task, has not been an exception to this [6]. Indeed, since 2015, research on plant diseases detection has strongly veered towards using deep learning. AlexNet, GoogLeNet, VGG, ResNet, and DenseNet deep learning models are commonly used [7]. [PERSON] [8] identifies a mildew disease in crop millet and takes VGG16 as a pre-trained model with ImageNet as a source dataset. The method shows the effectiveness of transferlearning for disease classification with small data. The performance of the method gives 95.00% accuracy. It is not suitable for the identification of other plant diseases. Within the plantvillage data set, [PERSON] trains plant diseases identification model and achieves an accuracy of 99.35% [9]. However, when tested on images taken under conditions different from the images used for training, the model's accuracy is reduced substantially. In [10], the authors fine-tune pretrained deep convolutional neural networks of AlexNet, GoogLeNet, and VGGNet using the LifeCLEF 2015 plant task dataset.They have improved the overall validation accuracy of the top system by 15% points while outperforming the top three competition participants in all categories. In [11], fine-tuning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease classification are performed. DenseNets obtains a test accuracy score of 99.75% for the 30 th epoch, beating the rest of the architectures. This research needs to be done to improve on the computational time and training process. As reported in [12], the authors focused on techniques to achieve an accuracy score of over 93% with class weight, SMOTE (Synthetic Minority Over-sampling Technique), and focal loss with deep convolutional neural networks from scratch. The goal was to counter high-class imbalance so that the model can accurately predict underrepresented classes. Their dataset biased towards Cassava Mosaic Disease and Cassava Brown Streak Virus Disease classes. They need further research though for multiple diseases on the same plant and multiple diseases on different plants. Training CNNs (Convolutional Neural Networks) to achieve high multiple plant diseases identification accuracy was very challenging due to two reasons: (1) Deep learning is highly dependent on the dataset. However, there are few public data sets in the field of plant diseases identification. (2) The deep learning model has more network layers and parameters resulting in more time and cost for training and validation. (3) Existing methods only focus on single-target and few-target plant diseases image with simple backgrounds. In real life, crop diseases have the characteristics of various types, large quantity, and complex backgrounds.