Plant Species Detection Using CNN Deployed on Android App

Abstract

There are hundreds of kinds of trees in the natural ecosystem, and it can be very difficult to distinguish between them. Botanists those who study plants however, are able to identify the type of tree at a glance by using the characteristics of a leaf. Plant identification is not exclusively the job of botanists and plant ecologists. It is required or useful for large parts of society, from professionals (such as landscape architects, foresters, farmers, conservationists, and biologists) to the general public (like Eco tourists, hikers, and nature lovers). But the identification of plants by conventional means is difficult, time consuming, and frustrating for novices. Machine learning is used to automatically classify leaf types. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. Deep learning is itself a self-learning technique used on large amounts of data, and recent developments in hardware and big data have made them more practical. We propose a method to classify plants (their species, diseases, uses etc.) using the CNN model, which is often used when applying deep learning to image processing. Crop disease is a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification.