Proceedings Article | 17 October 2023
Roberto Cilli, Ester Pantaleo, Giacinto Donvito, Marica Antonacci, Leonardo Cristella, Gioacchino Vino, Gaetano Alessandro Vivaldi, Vincenzo Giannico, Vincenzo D'Avino, Vito Amoruso, Cosimo Cagnazzo, Giovanni Sanesi, Manuela Matarrese, Nicola Amoroso, Roberto Bellotti
KEYWORDS: Cross validation, Machine learning, Deep convolutional neural networks, Land cover, Random forests, Performance modeling, Image classification, Image segmentation, Crop monitoring
Precision farming and remote sensing have seen an unprecedented development in the last decade. The growing interest in this domain has led to the development of robust and accurate processing pipelines to evaluate nutrient management and irrigation practices, among others. Problems such as crop classification have gained significant attention in Southern Italy due to unique challenges such as water scarcity and the spread of cultivar-specific diseases (i.e., Xylella fastidiosa). Here, we present a technological platform hosted by the ReCaS HTC/HPC cluster based in Bari, Italy, for the automated segmentation of common crops in Southern Italy, specifically the Apulia region, in very high-resolution aerial (VHR) RGB images. In particular, we discuss the adoption of a Deep Convolutional Neural Network (DCNN) which uses a lightweight EfficientNet-B0 architecture, for patch-wise land cover classification and compare its performance with a standard machine learning algorithm (Random Forest) fed with Haralick features. The DCNN, pre-trained on ImageNet-1000 and fine-tuned on a 4-class problem, including vineyard, olive groves, arable land, and “no-crop”, had the highest performance with an overall accuracy of 77±5 % when performing a repeated spatial cross-validation. The experimental results demonstrate the effectiveness of the proposed approach in achieving high accuracy in land cover classification, although a misclassification between arable land and “no-crop” was observed, as they share similar vegetation textural patterns. The lightweight EfficientNet-B0 architecture provides a good balance between accuracy and computational efficiency, making it a suitable choice for processing very high-resolution aerial images. The processing pipeline has been successfully implemented and deployed on the high-performance computing (HPC) platform, leveraging Apache Mesos as the underlying framework. To ensure efficient execution of tasks, the Chronos job scheduler has been employed to submit the execution of Docker containers. By utilizing specialized hardware, including Nvidia V100 and A100 GPUs, the pipeline can effectively handle and process substantial volumes of data within tight timeframes. The proposed approach is highly versatile and can be easily adapted to various precision farming applications. The use of Docker technology facilitates easy deployment and portability across different environments. Additionally, the adoption of a lightweight DCNN architecture allows to efficiently exploit parallel computing resources enabling seamless scalability and, therefore, handling of massive computational tasks across broader regions of interest.
