Soybean Yield Preharvest Prediction Based on Bean Pods and Leaves Image Recognition Using Deep Learning Neural Network Combined With GRNN

Soybean yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. The earlier the prediction during the growing season the better. Accurate soybean yield prediction is important for germplasm innovation and planting environment factor improvement. But until now, soybean yield has been determined by weight measurement manually after soybean plant harvest which is time-consuming, has high cost and low precision. This paper proposed a soybean yield in-field prediction method based on bean pods and leaves image recognition using a deep learning algorithm combined with a generalized regression neural network (GRNN). A faster region-convolutional neural network (Faster R-CNN), feature pyramid network (FPN), single shot multibox detector (SSD), and You Only Look Once (YOLOv3) were employed for bean pods recognition in which recognition precision and speed were 86.2, 89.8, 80.1, 87.4%, and 13 frames per second (FPS), 7 FPS, 24 FPS, and 39 FPS, respectively. Therefore, YOLOv3 was selected considering both recognition precision and speed. For enhancing detection performance, YOLOv3 was improved by changing IoU loss function, using the anchor frame clustering algorithm, and utilizing the partial neural network structure with which recognition precision increased to 90.3%. In order to improve soybean yield prediction precision, leaves were identified and counted, moreover, pods were further classified as single, double, treble, four, and five seeds types by improved YOLOv3 because each type seed weight varies. In addition, soybean seed number prediction models of each soybean planter were built using PLSR, BP, and GRNN with the input of different type pod numbers and leaf numbers with which prediction results were 96.24, 96.97, and 97.5%, respectively. Finally, the soybean yield of each planter was obtained by accumulating the weight of all soybean pod types and the average accuracy was up to 97.43%. The results show that it is feasible to predict the soybean yield of plants in situ with high precision by fusing the number of leaves and different type soybean pods recognized by a deep neural network combined with GRNN which can speed up germplasm innovation and planting environmental factor optimization.

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A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽

10.3390/electronics10010081 ◽

2021 ◽

Vol 10 (1) ◽

pp. 81

Author(s):

Jianbin Xiong ◽

Dezheng Yu ◽

Shuangyin Liu ◽

Lei Shu ◽

Xiaochan Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Plant Species ◽

Image Recognition ◽

Recurrent Neural Network ◽

Plant Diseases ◽

Learning Methods ◽

Smart Agriculture ◽

Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Frontiers in Research Metrics and Analytics ◽

10.3389/frma.2021.673928 ◽

2021 ◽

Vol 6 ◽

Author(s):

Tahani Aljohani ◽

Alexandra I. Cristea

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Methodological Approach ◽

High Accuracy ◽

Directional Model ◽

Textual Representations ◽

The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

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A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Mathematics ◽

10.3390/math7100898 ◽

2019 ◽

Vol 7 (10) ◽

pp. 898 ◽

Cited By ~ 12

Author(s):

Suhwan Ji ◽

Jongmin Kim ◽

Hyeonseung Im

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Learning Methods ◽

The Public ◽

Price Prediction ◽

Profitability Analysis ◽

The Media ◽

Long Short Term Memory

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages

Remote Sensing ◽

10.3390/rs11131584 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1584 ◽

Cited By ~ 18

Author(s):

Yang Chen ◽

Won Suk Lee ◽

Hao Gan ◽

Natalia Peres ◽

Clyde Fraisse ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Detection System ◽

Growth Cycle ◽

Aerial Images ◽

Accurate Estimation ◽

Yield Prediction ◽

Distribution Maps ◽

Detection And Counting

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.

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CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0189.1 ◽

2020 ◽

Vol 37 (9) ◽

pp. 1661-1668

Author(s):

Min Wang ◽

Shudao Zhou ◽

Zhong Yang ◽

Zhanhua Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Recognition ◽

Learning Ability ◽

Visualization Method ◽

Classification Methods ◽

Recognition Method ◽

Cloud Classification ◽

Average Accuracy

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

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Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

The Scientific World JOURNAL ◽

10.1155/2016/3460293 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Lukas Falat ◽

Dusan Marcek ◽

Maria Durisova

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Decision Making ◽

Soft Computing ◽

Clustering Algorithm ◽

Prediction Models ◽

Moving Average ◽

Series Data ◽

Decision Making Process ◽

Hybrid Neural Network

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined withK-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

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DeepGS: Predicting phenotypes from genotypes using Deep Learning

10.1101/241414 ◽

2017 ◽

Cited By ~ 7

Author(s):

Wenlong Ma ◽

Zhixu Qiu ◽

Jie Song ◽

Qian Cheng ◽

Chuang Ma

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Linear Regression Analysis ◽

Hidden Variables ◽

R Package ◽

Breeding Strategy ◽

Simulation Experiments ◽

Linear Relationships ◽

Genome Wide

AbstractMotivationGenomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.ResultsWe present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.AvailabilityDeepGS is provided as an open source R package available at https://github.com/cma2015/DeepGS.

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LSTM Neural Network for Textual Ngrams

10.20944/preprints201811.0579.v1 ◽

2018 ◽

Author(s):

Shaun C. D'Souza

Keyword(s):

Neural Network ◽

Deep Learning ◽

Human Brain ◽

Cognitive Neuroscience ◽

Probabilistic Models ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Language Models ◽

Brain Functions ◽

The Web

Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.

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Side Scan Sonar Shipwreck Image Recognition Algorithm based on Transfer Learning and Deep Learning

E3S Web of Conferences ◽

10.1051/e3sconf/202129002020 ◽

2021 ◽

Vol 290 ◽

pp. 02020

Author(s):

Boyu Zhang ◽

Xiao Wang ◽

Shudong Li ◽

Jinghua Yang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Image Recognition ◽

Correlated Data ◽

Small Sample ◽

Side Scan Sonar ◽

Transfer Training ◽

Small Sample Sizes

Current underwater shipwreck side scan sonar samples are few and difficult to label. With small sample sizes, their image recognition accuracy with a convolutional neural network model is low. In this study, we proposed an image recognition method for shipwreck side scan sonar that combines transfer learning with deep learning. In the non-transfer learning, shipwreck sonar sample data were used to train the network, and the results were saved as the control group. The weakly correlated data were applied to train the network, then the network parameters were transferred to the new network, and then the shipwreck sonar data was used for training. These steps were repeated using strongly correlated data. Experiments were carried out on Lenet-5, AlexNet, GoogLeNet, ResNet and VGG networks. Without transfer learning, the highest accuracy was obtained on the ResNet network (86.27%). Using weakly correlated data for transfer training, the highest accuracy was on the VGG network (92.16%). Using strongly correlated data for transfer training, the highest accuracy was also on the VGG network (98.04%). In all network architectures, transfer learning improved the correct recognition rate of convolutional neural network models. Experiments show that transfer learning combined with deep learning improves the accuracy and generalization of the convolutional neural network in the case of small sample sizes.

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Research on Image Recognition System Based on Computer Deep Learning Neural Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2066/1/012009 ◽

2021 ◽

Vol 2066 (1) ◽

pp. 012009

Author(s):

Shuqiang Du

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Recognition ◽

Recognition System ◽

Picture Recognition ◽

Deep Learning Neural Network ◽

Depth Learning

Abstract The selection and extraction of image recognition by artificial means needs more complicated work, which is not conducive to the recognition and extraction of important features. Deep learning and neural network represent the iterative expansion of computer intelligent tech, and bring significant results to image recognition. Based on this, this paper first gives the concept and model of neural network, then studies the utilization of deep learning neural network in image recognition, and finally analyses the picture recognition system on account of in-depth learning neural network.

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