Impact of Training Data Distribution on Plant Diseases Recognition Based on Deep Learning

Abstract

【Objective】The study was carried out to improve the accuracy of the deep learning model through adjusting the distribution of training dataset of lab-condition and field-condition images, to reduce the dependence of plant diseases recognition models on field-condition data.【Method】The plant diseases recognition model was optimized through adjusting the distribution of images of lab-conditions and field-conditions in training datasets. Deep learning models of plant diseases trained by three artificial neural networks of ResNeSt-50, VGG-16 and ResNet-50 were tested and compared.【Result】In a training dataset composed of a certain number of plant disease images, it had an impact on the model accuracy through adjusting the distribution of images of different conditions. When the proportion of images of the fieldconditions reached 30%, the accuracy of the model was improved by more than 18%. Through adding field-conditions images at a number ratio of 30% into a training dataset composed of 100% lab-condition images, the accuracy of the model was improved by more than 17%. Through adding lab-conditions images into a training dataset composed of 100% field-condition images, the accuracy of the model was improved with the increasing number of images, and the improved ranges were between 2% and 4%.【Conclusion】This method is suitable for the rapid establishment of high-accuracy plant diseases recognition models in the complex agricultural environment. It could reduce the dependence of plant recognition models on field-condition images, shorten the field data collection cycle at the beginning of model establishment and reduce the cost of field-condition images collection. It promotes a more effective application of artificial intelligence in unmanned farms and smart agriculture.

Keywords

• plant disease
• deep learning
• convolutional neural network
• data distribution
• lab-condition
• field-condition