Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves

Abstract

Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes of images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor for determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) using different capture devices to generate a database of images composed of laboratory-grown maize plants that were induced to either total phosphorus deficiency, medium deficiency, or total nutrition; (2) processing the images with state-of-the-art transfer learning architectures (i.e., VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the DenseNet201 model achieves superior performance, with 96% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf nutrition deficiency detection tools. The proposed method can be a starting point to fine-tune machine-vision-based solutions tailored for real-time monitoring of crop nutritional status.

Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants

“…The superior classification performance of the model using ResNet-50 may be related to the characteristics presented in this convnet. In this architecture, there are blocks with convolutional layers in sequence and a separate parallel identity layer [43], which can result in a more precise extraction of color or texture features from plant leaf images [44]. In the ResNet-50 used, the technique of transfer learning, combined with pre-training on large datasets, was applied, which conferred an essential generalization capacity for classifying different nitrogen concentrations in strawberry leaves [45].…”