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ردیف | عنوان | نوع |
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1 |
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021 Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding. Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception |
مقاله انگلیسی |
2 |
A deep learning approach to measure stress level in plants due to Nitrogen deficiency
یک روش یادگیری عمیق برای اندازه گیری سطح تنش در گیاهان به دلیل کمبود نیتروژن-2021 Stress due to nutrients deficiency in plants can reduce the agricultural yield significantly. Nitrogen, an essential nutrient, is a crucial growth-limiting factor and is the prime component of amino acids, proteins, nucleic acids, and chlorophyll. Nitrogen deficiency affects certain visible plant traits such as area, color, the number of leaves and plant height, etc. With the recent advancements in imaging technology, computer vision-based plant phenomics has become a promising field of plant research and management. Such imaging-based techniques are non-destructive and much faster with higher levels of automation. In this work, we have proposed an automatic image-based plant phenotyping approach for stress classification in plant shoot images. In this proposed phenotyping approach, a 23-layered deep learning technique is proposed and compared with traditional Machine Learning techniques and few other deep architectures. Results reveal that a simple 23-layered deep learning architecture is comparable to the established state of art deep learning architectures like ResNet18 and NasNet Large (having millions of trainable parameters) in yielding ceiling level stress classification from plant shoot images. In addition, the proposed model also outperforms traditional Machine Learning techniques by achieving an average of 8.25% better accuracy. Keywords: Computer vision | Deep learning | Nitrogen stress | Plant phenotyping |
مقاله انگلیسی |
3 |
Plant trait estimation and classification studies in plant phenotyping using machine vision – A review
برآورد و طبقه بندی صفات گیاهی در فنوتیپ سازی گیاهان با استفاده از بینایی ماشین - مرور-2021 Today there is a rapid development taking place in phenotyping of plants using
non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for
thousands of plants in the field. Plant phenotyping systems either use single imaging
method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high
resolution volumetric imaging. This paper provides an overview of imaging techniques
and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.
In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future
research directions related to the use of deep learning based machine vision algorithms
for structural (2-D and 3-D), physiological and temporal trait estimation, and classification
studies in plants. Keywords: Plant phenotyping | Machine vision | Plant trait estimation | Imaging techniques | Leaf segmentation and counting | Plant classification studies |
مقاله انگلیسی |