Monitoring the growth conditions and behavior of fish will enable scientific management, reduce the threat of losses caused by disease and stress. Traditional monitoring methods are time-consuming, laborious, and untimely monitoring readily leads to aquaculture accidents. As a non-invasive, objective, and repeatable tool, machine vision systems have been widely used in various aspects of aquaculture monitoring. Nevertheless, the complex underwater environment makes it difficult to obtain ideal data processing results only using traditional image processing methods. Due to their powerful feature extraction capabilities, deep learning (DL) algorithms have been widely used in underwater image processing. Hence, the combination of DL algorithms and machine vision for the automated monitoring of aquaculture is of great importance. As evidence for the multidisciplinary aspects of DL applications, attention is focused on the latest DL methods applied to five fields of research: classification, detection, counting, behavior recognition, and biomass estimation. Meanwhile, due to the low training efficiency of DL models caused by insufficient dataset, transfer learning and GAN have also put into spotlight of this filed to pursue high performance of DL models. We also present the challenges and benchmarks in terms of the advantages and disadvantages of the selected method in each field. In addition, we review the sources of image acquisition and pre-processing methods in aquaculture. Finally, the challenges and prospects of DL in aquaculture machine vision systems are discussed. The literature review shows that the deep neural networks such as AlexNet, LSTM, VGG, and GoogLeNet, have been used for aquaculture machine vision systems.

Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish

Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the scientific community on the use of DL for spectral data modelling. This paper provides a critical and comprehensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral data modelling. Although this work focuses on DL for the modelling of near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be expanded to cover other spectral techniques. Finally, empirical guidelines on the best practice for the use of DL for the modelling of spectral data are provided. 

Deep learning for near-infrared spectral data modelling: Hypes and benefits

Developing deep learning based regression approaches for prediction of firmness and pH in Kyoho grape using Vis/NIR hyperspectral imaging

Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes.In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization.This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping. 

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.

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Recent Advances in Multi- and Hyperspectral Image Analysis

Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can capture up to several hundred images of different wavelengths and offer relevant spectral signatures. Hyperspectral imaging technology has achieved breakthroughs in the acquisition of agricultural information and the detection of external or internal quality attributes of the agricultural product. Deep learning techniques have boosted the performance of hyperspectral image analysis. Compared with traditional machine learning, deep learning architectures exploit both spatial and spectral information of hyperspectral image analysis. To scrutinize thoroughly the current efforts, provide insights, and identify potential research directions on deep learning for hyperspectral image analysis in agriculture, this paper presents a systematic and comprehensive review. Firstly, its applications in agriculture are summarized, include ripeness and component prediction, different classification themes, and plant disease detection. Then, the recent achievements are reviewed in hyperspectral image analysis from the aspects of the deep learning models and the feature networks. Finally, the existing challenges of hyperspectral image analysis based on deep learning are summarized and the prospects of future works are put forward.

A review of deep learning used in the hyperspectral image analysis for agriculture

The invention discloses a field wheat high-throughput phenotype information obtaining device and a control method thereof. The device comprises a car frame, a measuring device and a control system, wherein the measuring device and the control system are fixedly arranged on the car frame, the car frame comprises a car frame body, a stretchable rod, a supporting frame, a first wheel and a second wheel, the measuring device comprises a network camera, an ultrasonic sensor, an NDVI sensor, a digital camera, a portable high spectrograph and an infrared distance measuring sensor, the control systemcomprises a computer, a controller and a stepping motor, the car frame body can be driven by the first wheel and the second wheel to move in spaces between lines of wheat in a wheat land, wheat phenotype information can be collected, the collected information can be sent to the computer, due to the fact that the device can freely move in the what land through the car body, information collection can be performed close to wheat, and compared with a traditional information collecting platform, information collecting accuracy can be achieved while the cost is lowered.

Field wheat high-throughput phenotype information obtaining device and control method thereof

The utility model discloses a field wheat high-throughput phenotypic information acquisition device The device comprises a frame, a measuring device and a control system, the measuring device and thecontrol system are fixedly arranged on the frame; wherein the frame comprises a frame body, a telescopic rod, a support frame, a first wheel and a second wheel; the measuring device includes: a measuring unit; the system comprises a network camera, an ultrasonic sensor, an NDVI sensor, a digital camera, a portable hyper-spectrometer and an infrared distance measuring sensor The control system includes: a controller; the device comprises a computer, a controller and a stepping motor, the frame body can be driven by the first wheels and the second wheels to move in a blank space between rows of wheat in a wheat field The wheat phenotype information can be collected, the collected information can be sent to the computer, the vehicle body can freely move on a wheat field and can be close towheat for information collection, and compared with a traditional information collection platform, the cost is reduced, and the accuracy of information collection can be achieved

Wang Chunying

Papers

A review of deep learning used in the hyperspectral image analysis for agriculture

Field wheat high-throughput phenotype information obtaining device and control method thereof

Field wheat high-throughput phenotypic information acquisition device