Recognition of soybean pods and yield prediction based on improved deep learning model

He, Haotian; Ma, Xiaodan; Guan, Haibing; Wang, Feiyi; Shen, Panpan

doi:10.3389/fpls.2022.1096619

Cited by 11 publications

(4 citation statements)

References 47 publications

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“…It is important to emphasize, however, that the high classification accuracies are almost always obtained on data with rather limited variability, so the reported results are not necessarily valid for real practical conditions (this issue is addressed in more depth in the next section). Disease recognition RGB DenseNet201 0.97 1 [23] Water stress detection RGB AlexNet, GoogLeNet, Inception V3 0.93 1 [24] Root phenotyping RGB CAE 0.66-0.99 [15] Weed detection RGB JULE, DeepCluster 0.97 1 [11] Volunteer corn detection RGB, CIR GoogleNet 0.99 1 [25] Disease severity RGB FPN, U-Net, DeepLabv3+ 0.95-0.98 [3] Pest detection HS Attention-ResNet 0.95 1 [26] Stem phenotyping RGB YOLO X 0.94 1 [27] Pod detection, yield prediction RGB YOLO v5 0.94 4 [28] Disease recognition RGB DCNN 0.98 1 [29] Seed counting RGB Two-column CNN 0.82-0.94 [30] Pest detection RGB Modified YOLO v4 0.87 2 [31] Disease severity RGB RetinaNet 0.64-0.65 1,2 [32] Weed detection RGB Faster R-CNN, YOLO v3 0.89-0.98 [19] Defoliation estimation RGB, synthetic AlexNet, VGGNet and ResNet 0.98 3 [33] Disease recognition RGB DIM-U-Net, SR-AE, LSTM 0.99 2 [34] Weed detection RGB DCNN 0.93 1 [35] Pest detection RGB Several CNNs 0.94 1 [12] Seed-per-pot estimation RGB DCNN 0.86 1 [36] Cultivar identification RGB ResNet-50, DenseNet-121, DenseNet 0.84 1 [37] Disease recognition RGB AlexNet, GoogLeNet, ResNet-50 0.94 1 [38] Pod counting RGB YOLO POD 0.97 4 [39] Seed phenotyping RGB, synthetic Mask R-CNN 0.84-0.90 [40] Yield prediction, biomass HS DCNN 0.76-0.91 [41] Disease recognition RGB GAN 0.96 1 [42] Disease recognition RGB Faster R-CNN 0.83 5 [43] Weed detection RGB Faster R-CNN 0.99 1 [44] Pod counting RGB R-CNN, YOLO v3, YOLO v4, YOLO X 0.90-0.98 [45] Seed defect recognition RGB MobileNet V2 0.98 1 [46] Seed counting RGB P2PNet-Soy 0.87 4 [47] Cultivar identification HS DCNN 0.90 1 …”

Section: Proximal Images As Main Input Datamentioning

confidence: 99%

Deep Learning for Soybean Monitoring and Management

Barbedo

2023

Seeds

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Artificial intelligence is more present than ever in virtually all sectors of society. This is in large part due to the development of increasingly powerful deep learning models capable of tackling classification problems that were previously untreatable. As a result, there has been a proliferation of scientific articles applying deep learning to a plethora of different problems. The interest in deep learning in agriculture has been continuously growing since the inception of this type of technique in the early 2010s. Soybeans, being one of the most important agricultural commodities, has frequently been the target of efforts in this regard. In this context, it can be challenging to keep track of a constantly evolving state of the art. This review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis of the main challenges and research gaps that still remain. The ultimate goal is to facilitate the leap from academic research to technologies that actually work under the difficult conditions found in the the field.

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Section: Proximal Images As Main Input Datamentioning

confidence: 99%

Deep Learning for Soybean Monitoring and Management

Barbedo

2023

Seeds

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“…A mathematical model was used for estimating the dependence of soybean seed yield losses upon cutting height variation [34], while soybean Simulation Model (SSM-iCrop2) and GIS were adopted to determine potential yield and the yield gap of soybean in Golestan Province of Iran [35]. The CROPGRO-Soybean was used for assessing soybean yield variability in the southeastern US [36] and for quantifying potential yield and yield gaps of soybean in the humid tropics of southwestern Ethiopia [37], and an improved deep learning model was applied to recognize the prediction of soybean yield [38]; etc.…”

Section: Introductionmentioning

confidence: 99%

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

Cai,

LV,

WEI

et al. 2024

PLoS ONE

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Global food security basically depends on potential yields of staple grain crops worldwide, especially under climate change. However, most scholars use various models of production function in which climatic factors are often considered to estimate crop yield mostly at local or regional level. Therefore, in this paper: Potential yields of rice, wheat, maize and soybean worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend Regressed (ARIMA-TR) model in which actual yields in recent two years are used for testing the reliability of projection and Gray System (GS) model for validating the test; Especially individual impacts of climate change on the productions of rice, wheat, maize and soybean worldwide since 1961 are analyzed by using unary regression model in which global mean temperature and land precipitation are independent variable while the yield of crop being dependent one, respectively. Results show that: by 2030, the ratio between average and top yields of world rice is projected to be 50.6% increasing, while those of world wheat, world maize and world soybean are projected to be 38.0% increasing, 14.7% decreasing and 72.5% increasing, respectively. Since 1961 global warming has exerted a negative impact on average yield of world rice less than on its top, a positive effect on average yield of world wheat while a negative impact on its top, a positive effect on average yield of world maize less than on its top, and a positive influence on average yield of world soybean while a negative one on its top, which might be slightly mitigated by ‘Carbon Peak’ target. The fluctuation of global rainfall contributes to the productions of these crops much less than global warming during same period. Our findings indicate that: to improve global production of four staple grain crops by 2030, the priorities of input should be given to either rice or wheat in both high and low yield countries, whereas to maize in high yield countries and to soybean in low yield countries. These insights highlight some difference from previous studies, and provide academia with innovative comprehension and policy-decision makers with supportive information on sustainable production of these four staple grain crops for global food security under climate change in the future.

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“…The final average detection supervision was 97.6% and the model parameters were compressed by 59.4%. He et al [17] proposed an output estimation method based on the number of soybean pods for multiple pods in a soybean plant. The YOLOv5 model was improved by embedding a CA attention mechanism and modifying the boundary regression loss function.…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Multi-Object Detection and Size Calculation for Blended Tobacco Shreds Using an Improved YOLOv7 Network and LWC Algorithm

Jia,

Niu,

Wang

et al. 2023

Sensors

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Detection of the four tobacco shred varieties and the subsequent unbroken tobacco shred rate are the primary tasks in cigarette inspection lines. It is especially critical to identify both single and overlapped tobacco shreds at one time, that is, fast blended tobacco shred detection based on multiple targets. However, it is difficult to classify tiny single tobacco shreds with complex morphological characteristics, not to mention classifying tobacco shreds with 24 types of overlap, posing significant difficulties for machine vision-based blended tobacco shred multi-object detection and unbroken tobacco shred rate calculation tasks. This study focuses on the two challenges of identifying blended tobacco shreds and calculating the unbroken tobacco shred rate. In this paper, a new multi-object detection model is developed for blended tobacco shred images based on an improved YOLOv7-tiny model. YOLOv7-tiny is used as the multi-object detection network’s mainframe. A lightweight Resnet19 is used as the model backbone. The original SPPCSPC and coupled detection head are replaced with a new spatial pyramid SPPFCSPC and a decoupled joint detection head, respectively. An algorithm for two-dimensional size calculation of blended tobacco shreds (LWC) is also proposed, which is applied to blended tobacco shred object detection images to obtain independent tobacco shred objects and calculate the unbroken tobacco shred rate. The experimental results showed that the final detection precision, mAP@.5, mAP@.5:.95, and testing time were 0.883, 0.932, 0.795, and 4.12 ms, respectively. The average length and width detection accuracy of the blended tobacco shred samples were −1.7% and 13.2%, respectively. The model achieved high multi-object detection accuracy and 2D size calculation accuracy, which also conformed to the manual inspection process in the field. This study provides a new efficient implementation method for multi-object detection and size calculation of blended tobacco shreds in cigarette quality inspection lines and a new approach for other similar blended image multi-object detection tasks.

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Recognition of soybean pods and yield prediction based on improved deep learning model

Cited by 11 publications

References 47 publications

Deep Learning for Soybean Monitoring and Management

Deep Learning for Soybean Monitoring and Management

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

A New Efficient Multi-Object Detection and Size Calculation for Blended Tobacco Shreds Using an Improved YOLOv7 Network and LWC Algorithm

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