Abiotic Stress Prediction from RGB-T Images of Banana Plantlets

Levanon, Sagi; Markovich, Oshry; Gozlan, Itamar; Bakhshian, Ortal; Zvirin, Alon; Honen, Yaron; Kimmel, Ron

doi:10.1007/978-3-030-65414-6_20

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…Surprisingly, adding thermal imagery did not improve results. Similarly, Levanon et al [120] utilized RGB and thermal imaging along with neural networks to predict water and nutrient stress in banana plantlets. The multi-modal data fusion approach enabled models to achieve high prediction accuracy of over 90% for four stress classes.…”

Section: F Crop Health Analysismentioning

confidence: 99%

“…However, techniques to integrate and fuse multi-modal data sources are still emergent. Capturing relationships between RGB, spectral, depth, thermal, and other data could significantly enhance model robustness and performance ( [120], [121]). Developing sensor fusion methods and tailored multi-modal networks is an open research frontier.…”

Section: ) Limited Integration Of Diverse Sensing Modalitiesmentioning

confidence: 99%

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A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Akbar,

Kamarulzaman,

Muzahid

et al. 2024

IEEE Access

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With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling datadriven decision-making. This paper provides a focused review of recent advancements in deep learningenabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.

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Section: F Crop Health Analysismentioning

confidence: 99%

Section: ) Limited Integration Of Diverse Sensing Modalitiesmentioning

confidence: 99%

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Akbar,

Kamarulzaman,

Muzahid

et al. 2024

IEEE Access

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“…Image-based measurement of plant traits has become ubiquitous, helping with understanding environmental impacts on plants, as well as aiding breeding programs and production of crops. Some important works on plant phenotyping include the 2 Plant Phenomics prediction of plant stresses [10], detection and segmentation of plants from aerial photography [11], and leaf or plant organ counting [12]. 3D reconstruction of plant matter is important in solving a number of core tasks including growth measurement and yield estimation such as seen in work by Moonrinta et al [13].…”

Section: Plant Phenotypingmentioning

confidence: 99%

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

et al. 2021

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3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.

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