Deep Learning Application in Plant Stress Imaging: A Review

Gao, Zongmei; Luo, Zhongwei; Zhang, Wen; Lv, Zhenzhen; Xu, Yanlei

doi:10.3390/agriengineering2030029

Cited by 85 publications

(49 citation statements)

References 80 publications

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“…rapid advance, spurring on the development of a decent range of agricultural remote sensing drone products [92,93]. In addition to advances in drone technology, it is equally important that development-oriented research of new phenotyping monitoring sensors, novel analysis tools and in-depth analysis of acquired data is achieved [45,50,94].…”

Section: Robotics and Eco-phenotyping In Open Field Settingsmentioning

confidence: 99%

Recent developments and potential of robotics in plant eco-phenotyping

Yao

Zedde

Kowalchuk

2021

Emerging Topics in Life Sciences

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Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide stepping stones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.

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Section: Robotics and Eco-phenotyping In Open Field Settingsmentioning

confidence: 99%

Recent developments and potential of robotics in plant eco-phenotyping

Yao

Zedde

Kowalchuk

2021

Emerging Topics in Life Sciences

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“…These advances have expanded the applicability of deep learning neural networks. Moreover, they have also demonstrated exceptional success on complex problems of plant phenotyping [ 19 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Wheat Fungi Diseases Identification by Deep Learning

Genaev

Сколотнева

Gultyaeva

et al. 2021

Plants

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Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.

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“…Several review papers on DL application in plant stress phenotyping have been published [27][28][29] showing the high potential of the technique; however, these papers have mainly focused on leaf-based biotic stress detection from the image processing point of view. This investigation aims to comprehensively review almost all the major sub-approaches of plant water stress assessment method connected to DL.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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Deep Learning Application in Plant Stress Imaging: A Review

Cited by 85 publications

References 80 publications

Recent developments and potential of robotics in plant eco-phenotyping

Recent developments and potential of robotics in plant eco-phenotyping

Image-Based Wheat Fungi Diseases Identification by Deep Learning

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

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