Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

Abstract

This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems.

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

“…Alshammari and Shahin [154] introduced Swin-TU-net, achieving 98.4% accuracy with low-cost sensors. Martínez-Ruedas et al [222] classified orchard management systems with DL, presenting overall accuracies exceeding 0.8. Šiljeg et al [169] compared ML models for olive tree crown extraction, where Geographic Object-Based Image Analysis-Support Vector Machine (GEOBIA-SVM) outperformed others.…”

“…Martínez-Ruedas et al [113] developed an automated methodology for inventory using Sentinel-2 imagery, characterizing 92% of Andalusian olive orchards. Martínez-Ruedas et al [232] also validated a DL approach using convolutional neural networks (CNNs), achieving 95.7% accuracy for sub-images and 82.6% at the farm level.…”