dataesr/scientific-paragraphs-categorization huggingface dataset:
The calibration has been performed for flows, tide levels, waves, and SSC (suspended sediment concentration) using data from two fixed in situ stations, Go-Cong and U-Minh, and 11 tide gauging stations (set up by the Vietnamese General Directorate of Hydro-Meteorology-VGDHM), 183 in situ (mobile) samplings performed in 2017's NE and 2016's SW monsoons. At Can-Tho and My-Thuan as the upstream boundaries, discharge and SSC values are imposed. At Vam Co, Soai-Rap, Dong Tranh-Rivers connecting to the Mekong Delta, discharges and SSC measured from VGDHM gauging stations and simulated from a 1D model are used as the other upstream boundaries of the Local Model. The Study Model covers the Go-Cong Coastal Zone connecting to the Soai-Rap Estuary, into which the Saigon-Dong Nai and Vam-Co Rivers discharge (Figure  2 ).
Numerical Model Results and Discussion Two protection structures have been chosen: T-shape breakwaters and sandbars with different configurations. No protective measure is considered as a baseline. The 2014 data have been used to simulate the impacts of the chosen structures because 2014 has been considered as a mean year on waves and river discharges. The results of typical two-month simulations in the NE and SW monsoons provided by both models, MIKE21 FM and TELEMAC, are discussed herein.
Funding: This study was funded by AFD and EU under contract number AFD-SIWRR 2016. Additional research was also supported by the Ministry of Science and Technology (MOST) in the project "Research on appropriate solutions and technologies to prevent erosion and stabilization of the coastline and estuaries of the Mekong River, from Tien Giang to Soc Trang province". This study was also partially funded by the NSERC-Discovery program (RGPIN-2018-0677).
(2)  The evaluation of the algorithm was based on our original database using a Phantom DJI pro 4 drone in different agricultural areas. (3)  The study of the temporal constraints was proposed based on the hardware/Software Co-Design approach. (4) A hardware acceleration was developed on several low-cost embedded architectures in order to respect the architectural and temporal constraints. Our algorithm has been evaluated on several embedded systems such as XU4, Jetson Nano, and Raspberry. The objective of these implementations is to study the adequacy between the hardware and the software. The results showed that the XU4 card remains the best choice for our application, thanks to its low consumption and cost, as well as the solid, parallel computation. The tools used in this evaluation are based on C/C++, OpenMP, and OpenCL.
The C/C++ language has been used to validate the algorithm proposed in the chosen system and OpenMP to exploit the parallelism in the selected card. For OpenCL, we used it to exploit the Graphics Processing Unit (GPU) part of our card for acceleration in order to minimize the processing time. The proposed algorithm has a complexity of O = K × (i × j) Log2 (i × j), where K is the number of data used and i × j is the image size. This study's data were based on the Souss Massa region's agriculture in the south of Morocco, which is considered one of the most productive regions of agriculture in Morocco. Our database was collected using UAV type DJI and RGB cameras for the fields of maize and orange, as well as a hand database for parsley and mint. Our paper is summarized as follows: the first part focuses on the different recent works and an overview of the RGB index used in agriculture.
The C/C++ language is generally limited in the context where we want to speed up the processing  [30] [31] [32]. For this reason, the OpenMP directive remains an excellent solution to accelerate the code in C/C++. On the other hand, CUDA and OpenCL offer a high-performance acceleration in heterogeneous systems type CPU-GPU for CUDA and CPU-GPU/FPGA/DSP for OpenCL. Despite its huge advantage, CUDA remains limited in heterogeneous systems due to its use only for Nvidia architecture, which encourages the use of OpenCL that gives flexibility in different architectures. For this reason, we have chosen to use OpenMP and OpenCL. The use of these languages as well as heterogeneous systems is still very limited in precision agriculture, as most of the works are based on software and workstations, which restrict the use of autonomous systems in real scenarios.
Area I The first area contains two types of products: mint and parsley, and it is located in the Souss Massa region in the south of Morocco. Mint is a highly effective plant that can grow up to 80 cm high. It belongs to the family of Lamiaceae. The most used compounds are menthol (between 35% and 55%) and menthone (10% to 40%). Mint has several types; in our case, we used the green mint. Just like parsley, it is a real mine of nutritional qualities. The collection of the images was done in February 2021 for mint and parsley. The agricultural area has a surface of 821 ha, which varies between mint and parsley. The choice of these two agricultural products is due to the great demand for them in this region as well as the great surface reserved for this type of product. These images were based on a hand database collection, which presents a low-cost technique of image collection.
The database contains more than 100 images of different positions in the agricultural area. Due to the large surface of the farming fields in the mentioned products, we have chosen two small surfaces to make our study. The first field is mint, which contains a total surface of 1. 27 ha, and the second one is parsley, with 3. 08 ha calculated with GPS, which implies 12,700 m 2 for the mint and 30,800 m 2 for the parsley. The two selected fields are located between Lat 30°22′01" N, Long 9°29′32" W, and Lat 30°22′01" N, Long 9°29′24" W, for ↔, as well as between Lat 30°22′05" N, Long 9°29′24" W and Lat 30°22′15" N, Long 9°29′26" W for ↕. Figure  2  shows the location of selected fields.
The database contains more than 100 images of different positions in the agricultural area. Due to the large surface of the farming fields in the mentioned products, we have chosen two small surfaces to make our study. The first field is mint, which contains a total surface of 1. 27 ha, and the second one is parsley, with 3. 08 ha calculated with GPS, which implies 12,700 m 2 for the mint and 30,800 m 2 for the parsley. The two selected fields are located between Lat 30 Figure  2  shows the location of selected fields. For the mint field, it is separated into small squares of 2 m × 2 m throughout the area, which can give in the high season up to seven packets for each square. For the parsley, we have a rectangle of 3 m × 1. 5 m with ten boxes approximately, according to the experience of the farmers in the region.
The distance between the rows is 60 to 80 cm, with a space of 13 to 21 cm between each plant. Our study has chosen three fields, two for the maize and one for the oranges. The field of the oranges For the mint field, it is separated into small squares of 2 m × 2 m throughout the area, which can give in the high season up to seven packets for each square. For the parsley, we have a rectangle of 3 m × 1. 5 m with ten boxes approximately, according to the experience of the farmers in the region. Figure  3  shows images of the database of the first area. Part 1 in Figure  3  shows the mint, and part 2, the parsley. The database was collected using a Samsung SM-J8 10F camera with a resolution of 5256 × 3790 for the first database, which contains the two agricultural products mint and parsley. For the mint field, it is separated into small squares of 2 m × 2 m throughout the area, which can give in the high season up to seven packets for each square.
For the parsley, we have a rectangle of 3 m × 1. 5 m with ten boxes approximately, according to the experience of the farmers in the region. Figure  3  shows images of the database of the first area. Part 1 in Figure  3  shows the mint, and part 2, the parsley. The database was collected using a Samsung SM-J8 10F camera with a resolution of 5256 × 3790 for the first database, which contains the two agricultural products mint and parsley.
45 km. The selected fields are located between 30°26′51" N, Long 9°01′10" W and 30°26′51" N, Long 9°00′00" W for ↔, and from 30°26′51" N, Long 9°00′00" W until 30°27′27" N, Long 9°00′00" W for ↕. Figure  4  shows the location of the second study area. The second database was collected by an unmanned aerial vehicle based on an RGB camera. The type of camera is DJI model FC6310R with a resolution of 5472 × 3648. The type of UAV used is DJI Phantom Pro 4. Figure  5  shows the two fields of maize and orange. The second database was collected by an unmanned aerial vehicle based on an RGB camera. The type of camera is DJI model FC6310R with a resolution of 5472 × 3648. The type of UAV used is DJI Phantom Pro 4. Figure  5  shows the two fields of maize and orange. with a perimeter of 1. 82 km, and for the second field of maize, we have an area of 9.
34 ha with a perimeter of 1. 45 km. The selected fields are located between 30°26′51" N, Long 9°01′10" W and 30°26′51" N, Long 9°00′00" W for ↔, and from 30°26′51" N, Long 9°00′00" W until 30°27′27" N, Long 9°00′00" W for ↕. Figure  4  shows the location of the second study area. The second database was collected by an unmanned aerial vehicle based on an RGB camera. The type of camera is DJI model FC6310R with a resolution of 5472 × 3648. The type of UAV used is DJI Phantom Pro 4. Figure  5  shows the two fields of maize and orange. Therefore, we can conclude that we have two databases, one collected by hand and the other using unmanned aerial vehicles. We have four agricultural products, two for the first database and two for the second. Figure  6  and Table  3  show the characteristics of each database.
Therefore, we can conclude that we have two databases, one collected by hand and the other using unmanned aerial vehicles. We have four agricultural products, two for the first database and two for the second. Figure  6  and Table  3  show the characteristics of each database. 3  shows the different specifications of the collected databases, i. e. , the tools used, the resolution of the images, the type of crop, and the altitude in the case of UAVs, as well as the surface of each field, where S1 is the surface of Maize 1 fields and S2 the surface of Maize 2 fields.
Methodologies Our methodology focuses on four steps to study a real scenario of a plant index monitoring system. Generally, we have the acquisition step, then the measurement and blur detection, the index calculation, and the image compression to reduce images size for storage. 3  shows the different specifications of the collected databases, i. e. , the tools used, the resolution of the images, the type of crop, and the altitude in the case of UAVs, as well as the surface of each field, where S1 is the surface of Maize 1 fields and S2 the surface of Maize 2 fields.
Image Acquisition The acquisition of the images was based on two low-cost RGB cameras in order to build the database that will be evaluated in the following paper. The collected images were divided into two databases: one collected by hand and the other with a UAV type DJI phantom Pro4 in two different areas. The UAV used for the acquisition has a Dual Frequency Control Signal of 2. 4 and 5. 8 Ghz, with a 7 km range, with a flight time of 30 min. The drone's weight is 1388 g, and it integrates a GPS/GLONASS. The precision of displacement of this tool is about ±0. 1 m for the vertical position and ±0. 3 m for the horizontal position. Each image collected by the UAV integrates the different information needed to show the type of camera used and the image's different characteristics (i. e. , focal length, exposure time, and other).
This test operation will help us pass the blur elimination procedure if the captured image does not have blur, minimizing the overall processing time. If the image contains blur, it will apply the Discrete Fourier Transform and the convolution product between the measured kernel gaussian DFT and the image DFT to recover the unblurred image based on the Inverse Discrete Fourier Transform. Then, this filtered image will be sent to the second algorithm to do the processing. Algorithm 1 presents the steps of blur detection and elimination. This part focuses on evaluating the indices based on the algorithm proposed in  [30]. The work aims to present an algorithm dedicated to vegetation monitoring based on multispectral databases and their implementation in real-time. The algorithm proposed in this paper is based on three functional blocks; the first block is for the preparation of images to calculate the indices.
Indexes Processing This part focuses on evaluating the indices based on the algorithm proposed in  [30]. The work aims to present an algorithm dedicated to vegetation monitoring based on multispectral databases and their implementation in real-time. The algorithm proposed in this paper is based on three functional blocks; the first block is for the preparation of images to calculate the indices. The second functional block is for Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) indices processing. The third block focuses on the thresholding operation. In this part of our work, we will focus on our proposed algorithm. The change that will be made in this part is the indices used. In our case, we will focus on Normalized Green-Red Difference Index (NGRDI) and Visible Atmospherically Resistant Index (VARI) based on an RGB camera.
Thus, this compression will allow it to take and use the memory in an optimal way. The compression algorithm begins as soon as the processing of the indexes finishes. Thus, it compresses the image that contains the RGB bands and not the images separated in R, G, and B bands. These bands will be deleted after the processing because they are not of interest after the indexes processing. Then, the storage procedure allows us to store the compressed RGB images and the images containing the indexes. At the end of the algorithm, it offers us two databases. One contains the original compressed and filtered images, and the other includes the images containing the indexes corresponding to each RGB image. Figure  7  shows the global algorithm separated into three blocks.
Sequential Implementation of the CPU-Based Algorith The implementation on the CPUs of the used architecture is generally done in a sequential mode. This implementation, in our case, is based on the C/C++ language. After the temporal evaluation of the different blocks, we proceed to the distribution of each block in functional blocks, which reflect the various treatments used in the chosen block. Table  7  shows the processing time consumption of each block in our algorithm. The time evaluation on several machines showed that the desktop consumes less time than the other tools used, giving a total time of 133. 6 ms to process each image. In the other part, we have the two embedded systems, Jetson nano, and XU4, which consume, respectively, 316. 4 and 386. 4 ms for the processing of each image.
FB1 focuses on searching optimal size to separate the image into 8*8 blocks and then convert it to 64 bits. FB2 takes care of the DCT application and the quantization, and FB3 for the histogram. Then FB4 and FB5 fill the Huffman table and the image coding based on these tables, respectively. The six functional blocks focus on the storage of the compressed images and the database management by applying a test to the image to see if it is an image that contains the different indices or a raw RGB image. After the specification of the different block functions, the time evaluation for the different blocks has to be applied in order to conclude which functional blocks consume more time. The time evaluation was based on the desktop, XU4, Jetson Nano, and Raspberry. Figure  11  shows the results obtained for each FB.
blocks and then convert it to 64 bits. FB2 takes care of the DCT application and the quantization, and FB3 for the histogram. Then FB4 and FB5 fill the Huffman table and the image coding based on these tables, respectively. The functional blocks focus on the storage of the compressed images and the database management by applying a test to the image to see if it is an image that contains the different indices or a raw RGB image. After the specification of the different block functions, the time evaluation for the different blocks has to be applied in order to conclude which functional blocks consume more time. The time evaluation was based on the desktop, XU4, Jetson Nano, and Raspberry. Figure  11  shows the results obtained for each FB. Figure  11  shows the results of the time evaluation for block 1 and 3; from the processing time analysis, we can conclude that in the case of block 1 (figure on the right), we have FB1 consuming 6.
CPU-GPU Boarding Based OpenCL and OpenMP Our second implementation was based on OpenCL and OpenMP to accelerate the functional blocks that take most of the time. In our study, we used both languages to ensure the exploitation of the CPU part using OpenMP and the different GPU cores using OpenCL. The acceleration in the CPU of the XU4 board was used for the compression part and OpenCL for the blur elimination and index processing part. Figure  12  shows the implementation model based on the acceleration on the GPU part using OpenCL. After the time evaluation shown in Table  8  and Figure  12 , we have concluded that FB4 in block 1 consumes the most processing time. For this reason, we have opted to accelerate this FB in the GPU part of the board. Figure  13  shows that after FB3, we call the kernel for running on the GPU part; in this case, the CPU part provides the necessary data for the execution.
The image on the right also shows the processing time for 100 frames of FB 4 in block 1. The time varies between a minimum value of 2. 24 ms and a maximum of 8. 2 ms, which gives an average of 5. 8 ms with an improvement ×12 compared to the sequential version, which consumes 70 ms. After improving blocks 1 and 2, we also enhanced block 3. Figures  14  and 15  show the results obtained. Our second implementation was based on OpenCL and OpenMP to accelerate the functional blocks that take most of the time. In our study, we used both languages to en sure the exploitation of the CPU part using OpenMP and the different GPU cores using OpenCL. The acceleration in the CPU of the XU4 board was used for the compression par and OpenCL for the blur elimination and index processing part. Figure  12  shows the im plementation model based on the acceleration on the GPU part using OpenCL.
For this reason, we have evaluated both databases based on these indices. The images used in this evaluation are based on images collected by UAV and hand data. Figure  18  shows images from the database used in this work. In Figure  18 , we have the different images used in our evaluation. On the top left, we have an agricultural field of maize collected by a UAV; on the top right, we have a field of orange trees also collected by a UAV. We have a parsley field on the bottom left, and on the right, the mint field. These data were evaluated using the indices listed in Table  1  in our case, we chose the two indices NGRDI and VARI. Figure  19  shows the results ob tained after the evaluation. In Figure  18 , we have the different images used in our evaluation. On the top left, we have an agricultural field of maize collected by a UAV; on the top right, we have a field of orange trees also collected by a UAV.
We have a parsley field on the bottom left, and on the right, the mint field. These data were evaluated using the indices listed in Table  1 ; in our case, we chose the two indices NGRDI and VARI. Figure  19  the results obtained after the evaluation. Figure  19  shows the evaluation of the NGRDI based on mint plants; image A shows the agricultural field, B is the green band, C is the red band, and D is the calculated index based on a threshold of 0. 12. Images E and F show the same calculated index but we varied the threshold; in this case, we used a threshold of 0. 45. Image G shows an index matrix generated by MATLAB to see the different values that exist in the image. Figure  20  shows the evaluation of parsley fields based on the NGRDI. Thus, we have evaluated the orange plants as shown in Figure  21.
In Figure  18 , we have the different images used in our evaluation. On the top lef have an agricultural field of maize collected by a UAV; on the top right, we have a of orange trees also collected by a UAV. We have a parsley field on the bottom left on the right, the mint field. These data were evaluated using the indices listed in Tab in our case, we chose the two indices NGRDI and VARI. Figure  19  shows the result tained after the evaluation. Figure  22  shows a comparison between NGRVI and VARI using parsley (in the left) and maize (in the right) plants. The image on the right shows the evaluation based on an image collected by UAV, and on the right, the database is collected by hand. The results showed that the VARI is more sensitive to vegetation than NGRVI based on the same threshold.
Conclusions and Future Work Real-time monitoring of agricultural fields requires a robust monitoring system that satisfies the time constraint as well as the many other requirements. The hybrid algorithm proposed in this work tries to address the different constraints such as memory saturation during processing as well as blur detection due to camera movement during capture. The evaluation was based on several benchmarks to validate the algorithm's implementation on several homogeneous low-cost embedded architectures such as the Raspberry board and heterogeneous ones such as Odroid XU4 and Jetson Nano. A hardware/Software Co-Design study followed the validation of the algorithm to conclude that the XU4 board remains the best choice in terms of processing time, power consumption, and low cost.
The evaluation results show that we can reach a processing performance up to 311 frames/s. Thus, the algorithm has been validated using our database collected with an RGB camera and a DJI Phantom Pro 4 drone. In terms of algorithmic complexity, the proposed algorithm has low complexity. The study of the temporal complexity has shown that the sequential implementation consumes a very high processing time that does not respect real-time. For this reason, hardware acceleration has been proposed to improve the proposed algorithm based on an acceleration factor of ×3. 3 compared to sequential implementation. This acceleration is based on OpenCL and OpenMP language in XU4 embedded architecture. Future work aims to integrate precise localization algorithms to improve the quality of monitoring in agricultural fields based on multi-sensor fusion algorithms and accurate localization.
Morphology study: Total epidermis and living epidermis thickness were quantified on HES histology images using a specific image processing tool designed to automatically identify 3 limits of the tissue: surface, stratum corneum/living epidermis limit and basal layer as recently reported  (Dimitrov et al. , 2021). Different color thresholds and morphological operators are used to segment RGB color nanozoomed images (large field view). These specific algorithms were introduced into a userfriendly dedicated software developed by the company ADCIS (Saint-Contest, France). This J o u r n a l P r e -p r o o f software allows us to process images in a high-throughput way, including quantification of the morphological parameters of the tissue, especially the thickness of each layer. Representative images were extracted using the viewing software NDP View (Hamamastu, Massy, France) for morphology observation.