An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle Imagery

TL;DR

This paper introduces ERI-CNN, a novel CNN model with random initialization, achieving high accuracy in recognizing endemic columnar cacti in UAV imagery, aiding environmental conservation efforts.

Contribution

The paper presents a new ERI-CNN model with enhanced random initialization for plant recognition, outperforming existing CNN architectures in accuracy and efficiency.

Findings

ERI-CNN achieves 98% accuracy in cactus recognition.

The model outperforms InceptionV3 and LeNet-5 in experiments.

High precision and recall demonstrate reliable recognition performance.

Abstract

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuac\'an-Cuicatl\'an Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss.