A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery

Molina, Miguel Ángel; Jiménez-Navarro, M. J.; Martínez‐Álvarez, Francisco; Asencio-Cortés, Gualberto

doi:10.1007/978-3-030-86271-8_43

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…As noted above, the monitoring of phenology and seedling growth using computer vision has received considerable attention in the literature [20,24,25,[34][35][36][37][38][39][40][41]. It is therefore important to relate our proposal to these related works.…”

Section: Discussionmentioning

confidence: 99%

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

et al. 2023

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This study explores the application of computer vision for enhancing the selection of rootstock-graft combinations and detecting diseases in grape seedlings. Computer vision has various applications in viticulture, but publications and research have not reported the use of computer vision in rootstock-graft selection, which defines the novelty of this research. This paper presents elements of the technology for applying computer vision to rootstock-graft combinations and includes an analysis of grape seedling cuttings. This analysis allows for a more accurate determination of the compatibility between rootstock and graft, as well as the detection of potential seedling diseases. The utilization of computer vision to automate the grafting process of grape cuttings offers significant benefits in terms of increased efficiency, improved quality, and reduced costs. This technology can replace manual labor and ensure economic efficiency and reliability, among other advantages. It also facilitates monitoring the development of seedlings to determine the appropriate planting time. Image processing algorithms play a vital role in automatically determining seedling characteristics such as trunk diameter and the presence of any damage. Furthermore, computer vision can aid in the identification of diseases and defects in seedlings, which is crucial for assessing their overall quality. The automation of these processes offers several advantages, including increased efficiency, improved quality, and reduced costs through the reduction of manual labor and waste. To fulfill these objectives, a unique robotic assembly line is planned for the grafting of grape cuttings. This line will be equipped with two conveyor belts, a delta robot, and a computer vision system. The use of computer vision in automating the grafting process for grape cuttings offers significant benefits in terms of efficiency, quality improvement, and cost reduction. By incorporating image processing algorithms and advanced robotics, this technology has the potential to revolutionize the viticulture industry. Thanks to training a computer vision system to analyze data on rootstock and graft grape varieties, it is possible to reduce the number of defects by half. The implementation of a semi-automated computer vision system can improve crossbreeding efficiency by 90%. Reducing the time spent on pairing selection is also a significant advantage. While manual selection takes between 1 and 2 min, reducing the time to 30 s using the semi-automated system, and the prospect of further automation reducing the time to 10–15 s, will significantly increase the productivity and efficiency of the process. In addition to the aforementioned benefits, the integration of computer vision technology in grape grafting processes brings several other advantages. One notable advantage is the increased accuracy and precision in pairing selection. Computer vision algorithms can analyze a wide range of factors, including size, shape, color, and structural characteristics, to make more informed decisions when matching rootstock and graft varieties. This can lead to better compatibility and improved overall grafting success rates.

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Section: Discussionmentioning

confidence: 99%

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

et al. 2023

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“…Model-based transfer learning is a widely utilized technique in transfer learning. This strategy involves transferring knowledge from a pre-existing model, known as the source model, to a newly developed model called the target model ( Molina et al, 2021 ). This technique aims to enhance the performance of the target model on a task or domain that is related but distinct from the original domain.…”

Section: Introductionmentioning

confidence: 99%

“…In feature-based transfer learning, the knowledge is transferred from an already-trained model (source model) to a new model (target model) with the help of the feature representations that the source model learned ( Chou, Wang & Lo, 2023 ; Molina et al, 2021 ; Shan et al, 2021 ). Extracting and reusing pertinent features from the source model to boost the performance of the target model is the core emphasis of feature-based transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Offloading the computational complexity of transfer learning with generic features

Safdar Ali Khan,

Husen,

Nisar

et al. 2024

PeerJ Computer Science

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Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously.

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“…Nevertheless, applications of CV and DL in phenology monitoring are mostly focused on arable crops (e.g., rice, barley, maize) [29][30][31][32], orchards [33,34], and forest trees [35][36][37] with limited studies into vegetable crops. Additionally, the developed models are cropspecific and tend to be based on well-defined phenophases, such as flowering [13,33,35], neglecting the spatiotemporal phenology dynamics.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery

Cited by 4 publications

References 15 publications

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

Offloading the computational complexity of transfer learning with generic features

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

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