Improving plant disease recognition with generative adversarial network under limited training set

This thesis introduces a generative adversarial network (GAN) based method to classify diseased images using a limited training set. A general introduction of machine learning applications in the agriculture domain is provided. The issue of plant disease recognition has been investigated in this thesis. First, the successful applications of convolutional neural networks (CNNs) to plant disease classification have been reviewed. It is found out that most of the methods are built under the assumption that there is enough training set. The issue of limited training data is overlooked. Thus, the over-fitting problem caused by a limited training set is discussed. Second, a new approach is proposed to solve the limited training set problem. The proposed method consists of four parts: CNN, data augmentation, GAN and label smoothing regularization (LSR). CNN is used to classify plant diseases and species. Data augmentation and GAN are used to generate additional samples for training. LSR technique can help the model avoid the over-fitting problem. Finally, three comparison experiments have been designed. The analysis proves the effectiveness of the proposed method. Compared with using the real dataset only, the proposed method improves the prediction accuracy by 6%

A transformer-based approach for early prediction of soybean yield using time-series images

Crop yield prediction which provides critical information for management decision-making is of significant importance in precision agriculture. Traditional manual inspection and calculation are often laborious and time-consuming. For yield prediction using high-resolution images, existing methods, e.g., convolutional neural network, are challenging to model long range multi-level dependencies across image regions. This paper proposes a transformer-based approach for yield prediction using early-stage images and seed information. First, each original image is segmented into plant and soil categories. Two vision transformer (ViT) modules are designed to extract features from each category. Then a transformer module is established to deal with the time-series features. Finally, the image features and seed features are combined to estimate the yield. A case study has been conducted using a dataset that was collected during the 2020 soybean-growing seasons in Canadian fields. Compared with other baseline models, the proposed method can reduce the prediction error by more than 40%. The impact of seed information on predictions is studied both between models and within a single model. The results show that the influence of seed information varies among different plots but it is particularly important for the prediction of low yields

Benralizumab efficacy and safety in severe asthma: A randomized trial in Asia

Background: Benralizumab is indicated as add-on therapy in patients with uncontrolled, severe eosinophilic asthma; it has not yet been evaluated in a large Asian population with asthma in a clinical trial. Objective: To evaluate the efficacy and safety of benralizumab in patients with severe asthma in Asia. Methods: MIRACLE (NCT03186209) was a randomized, Phase 3 study in China, South Korea, and the Philippines. Patients aged 12–75 years with severe asthma receiving medium- to high-dose inhaled corticosteroid/long-acting β2-agonists, stratified (2:1) by baseline blood eosinophil count (bEOS) (≥300/μL; <300/μL), were randomized (1:1) to benralizumab 30 mg or placebo. Endpoints included annual asthma exacerbation rate (AAER; primary endpoint), change from baseline at Week 48 in pre-bronchodilator (BD) forced expiratory volume in 1 s (FEV1 is being defined, not BD, which has already been defined) and total asthma symptom score (TASS). Safety was evaluated ≤Week 56. Results: Of 695 patients randomized, 473 had baseline bEOS ≥300/μL (benralizumab n = 236; placebo n = 237). In this population, benralizumab significantly reduced AAER by 74% (rate ratio 0.26 [95% CI 0.19, 0.36], p <0.0001) and significantly improved pre-BD FEV1 (least squares difference [LSD] 0.25 L [95% CI 0.17, 0.34], p <0.0001) and TASS (LSD −0.25 [−0.45, −0.05], p = 0.0126) versus placebo. In patients with baseline bEOS <300/μL, there were numerical improvements in AAER, pre-BD FEV1, and TASS with benralizumab versus placebo. The frequency of adverse events was similar for benralizumab (76%) and placebo (80%) in the overall population. Conclusions: MIRACLE data reinforces the efficacy and safety of benralizumab for severe eosinophilic asthma in an Asian population, consistent with the global Phase 3 results

Deep learning approaches for yield prediction and crop disease recognition

The increase of the world population has brought significant challenges to the agriculture production system. Although mechanization has been realized in agriculture, many tasks (e.g., breeding, field inspection) are still labor-intensive and time-consuming. Therefore an automatic and intelligent solution is needed for the advancement of agricultural production. During this process, the biggest challenge is how to teach computers to understand the concepts in the real world. For example, an experienced expert can easily determine whether a plant is diseased or healthy. However, this may be challenging for the computer. Thus, the motivation of this dissertation study is to tackle these challenges in precision agriculture. This dissertation consists of four papers that propose different deep learning methods for the most challenging problems in agriculture. In the first paper, a genetic algorithm (GA)-assisted deep neural network was built for yield prediction using genetic information and environmental factors. In the global search phase, the GA was introduced to help determine the best initial weights of the neural network. In the local phase, random perturbation was used to avoid the local optimum. By using the proposed method, the root mean square error can be reduced by up to 10%. In the second paper, we proposed a generative adversarial network (GAN)-based approach to generate additional images for the classification of plant species and diseases using limited data. CNN was used as the basic network to classify species and diseases. GAN and label smoothing regularization (LSR) were combined to generate additional training images. Regular data augmentation techniques were also used to expand the dataset. The results showed that compared with using the real dataset only, the proposed method can improve the prediction accuracy by 6%. In the third paper, the potential of using satellite imagery for plant disease detection was explored. A gated recurrent units (GRU)-based model was presented for early detection of soybean sudden death syndrome (SDS) through time-series satellite imagery. The results showed that, compared to XGBoost and fully connected deep neural network (FCDNN), the GRU-based can improve the overall prediction accuracy by 7%. In addition, the proposed method can also be adapted to predict the future development of SDS. In the fourth paper, a transformer-based approach was proposed for soybean yield prediction using time-series camera images and seed treatments information. First, a vision transformer (ViT) base model was designed to extract features from the images. Then another transformer-based model was established to predict the yield using the time-series features. A case study was been conducted using a data set that was collected during the 2020 soybean-growing seasons in Canada. The experiment results show that compared to non-time series prediction and other baseline models, the proposed approach can reduce the mean squared error by 25%-40%. In conclusion, this dissertation aims to apply different state-of-art deep learning methods in agriculture. The study covers different topics, which range from yield prediction, species classification, to plant disease classification and prediction. At the model level, the application of linear models, tree-based methods, fully connected neural networks, convolutional neural networks, time-series models and transformers to different tasks have been investigated. In terms of the learning type, both unsupervised learning and supervised learning have been utilized. The experimental results have shown that appropriate deep learning methods can achieve better performance than traditional methods on specific tasks. Based on our work, more applications of deep learning techniques can be developed in the future

Improving plant disease recognition with generative adversarial network under limited training set

This thesis introduces a generative adversarial network (GAN) based method to classify diseased images using a limited training set. A general introduction of machine learning applications in the agriculture domain is provided. The issue of plant disease recognition has been investigated in this thesis. First, the successful applications of convolutional neural networks (CNNs) to plant disease classification have been reviewed. It is found out that most of the methods are built under the assumption that there is enough training set. The issue of limited training data is overlooked. Thus, the over-fitting problem caused by a limited training set is discussed. Second, a new approach is proposed to solve the limited training set problem. The proposed method consists of four parts: CNN, data augmentation, GAN and label smoothing regularization (LSR). CNN is used to classify plant diseases and species. Data augmentation and GAN are used to generate additional samples for training. LSR technique can help the model avoid the over-fitting problem. Finally, three comparison experiments have been designed. The analysis proves the effectiveness of the proposed method. Compared with using the real dataset only, the proposed method improves the prediction accuracy by 6%.</p

Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.</jats:p

COVID-19 forecasting and intervention planning using gated recurrent unit and evolutionary algorithm

The rapid spread of COVID-19, caused by the SARS-CoV-2 virus, has had and continues to pose a significant threat to global health. We propose a predictive model based on the gated recurrent unit (GRU) that investigates the influence of non-pharmaceutical interventions (NPIs) on the progression of COVID-19. The proposed model is validated by case studies for multiple states in the United States. It should be noted that the proposed model can be generalized to other regions of interest. The results show that the predictive model can achieve accurate forecasts across the US. The forecast is then utilized to identify the optimal mitigation policies. The goal is to identify the best stringency level for each policy that can minimize the total number of new COVID-19 cases while minimizing the mitigation costs. A meta-heuristics method, named multi-population evolutionary algorithm with differential evolution (MPEA-DE), has been developed to identify optimal mitigation strategies that minimize COVID-19 infection cases while reducing economic and other negative implications. We compared the optimal mitigation strategies identified by the MPEA-DE model with three baseline search strategies. The results show that MPEA-DE performs better than other baseline models based on prescription dominance.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at DOI: 10.1007/s00521-022-07394-z. Copyright 2022 The Author(s). Posted with permission