Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning

Chacón-Maldonado, Andrés Manuel; Molina-Cabanillas, Miguel Angel; Troncoso, Alicia; Martínez‐Álvarez, Francisco; Asencio-Cortés, Gualberto

doi:10.1007/978-3-031-15471-3_24

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although crop growth models can simulate the timing of the phenophases of a particular genotype (individual), they cannot transpose the simulation to different spatial scales, as the heterogeneities in climate and management conditions are neglected [7][8][9]. Moreover, near-surface remote sensing techniques, such as digital repeat photography [10,11], and satellite remote sensing linked with vegetation indices (e.g., leaf area index) provide valuable data about phenology dynamics at a regional scale [12]. However, the ability of these methods to monitor phenology at the field or subfield scale is limited, especially in crops with hardly perceivable phenophase transitions such as vegetable crops [13].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Deep Learning as Tools for Leveraging Dynamic Phenological Classification in Vegetable Crops

et al. 2023

View full text Add to dashboard Cite

The efficiency of agricultural practices depends on the timing of their execution. Environmental conditions, such as rainfall, and crop-related traits, such as plant phenology, determine the success of practices such as irrigation. Moreover, plant phenology, the seasonal timing of biological events (e.g., cotyledon emergence), is strongly influenced by genetic, environmental, and management conditions. Therefore, assessing the timing the of crops’ phenological events and their spatiotemporal variability can improve decision making, allowing the thorough planning and timely execution of agricultural operations. Conventional techniques for crop phenology monitoring, such as field observations, can be prone to error, labour-intensive, and inefficient, particularly for crops with rapid growth and not very defined phenophases, such as vegetable crops. Thus, developing an accurate phenology monitoring system for vegetable crops is an important step towards sustainable practices. This paper evaluates the ability of computer vision (CV) techniques coupled with deep learning (DL) (CV_DL) as tools for the dynamic phenological classification of multiple vegetable crops at the subfield level, i.e., within the plot. Three DL models from the Single Shot Multibox Detector (SSD) architecture (SSD Inception v2, SSD MobileNet v2, and SSD ResNet 50) and one from You Only Look Once (YOLO) architecture (YOLO v4) were benchmarked through a custom dataset containing images of eight vegetable crops between emergence and harvest. The proposed benchmark includes the individual pairing of each model with the images of each crop. On average, YOLO v4 performed better than the SSD models, reaching an F1-Score of 85.5%, a mean average precision of 79.9%, and a balanced accuracy of 87.0%. In addition, YOLO v4 was tested with all available data approaching a real mixed cropping system. Hence, the same model can classify multiple vegetable crops across the growing season, allowing the accurate mapping of phenological dynamics. This study is the first to evaluate the potential of CV_DL for vegetable crops’ phenological research, a pivotal step towards automating decision support systems for precision horticulture.

show abstract