Deep Learning and Artificial Neural Network Methodology for Recognition and Identification of Plant

Plant segregation and crop identification help people to better understand and protect plants. The leaves of plants are the important parts of recognition. With the development of technological intelligence and mechanical detection technology, plant leaf recognition technology based on image analysis is used to improve plant integration knowledge and protection. In-depth learning is a summary of the in-depth neural network learning process and belongs to the structure of the neural network. It can learn features from big data and use an artificial neural network based on a back distribution algorithm to train and separate plant leaf samples. Contents of this paper extract the characteristics of plant leaves and identify plant species according to image analysis. First, images of plant leaves are categorized in different ways, and then an extraction algorithm is used to extract the leaf formations and texture characteristics in the leaf sample images. After that, the complete feature details of the plant leaves are compiled according to the full data. At the same time, comparing the leaves of 7 different plants found that ginkgo leaves were easy to spot. With photos of the leaves under a complex background, a good visual effect was obtained. Image samples of the test set are included in the learning model to detect reconstruction errors. The class label of the test set can be obtained by redesigning the deep learning model with the smallest error set.