Kumar T Ananth, Rajmohan R, Adeola Ajagbe Sunday, Gaber Tarek, Zeng Xiao-Jun, Masmoudi Fatma
Computer Science and Engineering, IFET College of Engineering, Valavanur, Viluppuram, India.
Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.
PLoS One. 2023 Aug 11;18(8):e0289963. doi: 10.1371/journal.pone.0289963. eCollection 2023.
Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.
由于棕榈树幼苗和幼株体型微小且缺乏明显的形态特征，对其进行监测面临着巨大挑战。人们需要能够为恢复专家提供高分辨率、快速且环保的棕榈树幼苗监测系统的技术方法。对幼株进行计数并将其鉴定到属级可能是一项极其耗时且具有挑战性的任务。事实证明，卷积神经网络（CNNs）在图像识别的许多方面都很有效；然而，其性能会因应用而异。现有的基于CNN的监测和预测幼株生长模型的性能还有进一步提升的空间。为实现这一目标，提出了一种新颖的带间隙层改进的CNN架构（GL-CNN），并结合了物联网有效监测系统和无人机技术。无人机用于捕捉幼株图像，物联网模型用于获取幼株健康状况的地面真实信息。所提出的模型经过训练，可针对给定的一组棕榈树幼株图像预测幼苗生长的成功与不佳情况。所提出的GL-CNN架构在定义的卷积层和为输出分类设计的间隙层方面具有新颖性。有两个64×3卷积层（conv层）、两个128×3 conv层、两个256×3 conv层和一个512×3 conv层用于处理输入图像。从间隙层获得的输出使用ReLU分类器进行调制，以确定幼苗分类。为评估所提出的系统，使用无人机技术实时收集了一个新的棕榈树幼株数据集。该数据集由棕榈树幼株的图像组成。评估结果表明，所提出的GL-CNN模型的表现优于现有的CNN架构，平均准确率达到95.96%。
