Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging
TL;DR: A new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs and the results show that the segmentation algorithm could segment wheat ears from a complex field background and the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion.
Abstract: Fusarium head blight (FHB) is one of the most important diseases in wheat worldwide. Evaluation and identification of effective fungicides are essential for control of FHB. However, traditional methods based on the manual disease severity assessment to evaluate the efficacy of fungicides are time-consuming and laborsome. In this study, we developed a new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs. Enhanced red-green-green (RGG) images were processed from acquired raw red-green-blue (RGB) images of wheat ear samples; the images were transformed in color spaces through K-means clustering for rough segmentation of wheat ears; a random forest classifier was used with features of color, texture, geometry and vegetation index for fine segmentation of disease spots in wheat ears; a newly proposed width mutation counting algorithm was used to count wheat ears; and the disease severity of the wheat ears groups was graded and the efficacy of six fungicides was evaluated. The results show that the segmentation algorithm could segment wheat ears from a complex field background. And the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion. The average counting accuracy of all and diseased wheat ears were 93.00% and 92.64%, respectively, with the coefficients of determination (R
2
) of 0.90 and 0.98, and the root mean square error (RMSE) of 10.56 and 7.52, respectively. The new method could accurately assess the diseased levels of wheat eat groups infected by FHB and determine the efficacy of the six fungicides evaluated. The results demonstrate a potential of using digital imaging technology to evaluate and identify effective fungicides for control of the FHB disease in wheat and other crop diseases.
Citations
More filters
TL;DR: The feasibility of rapidly determining levels of FHB in wheat spikes is demonstrated, which will greatly facilitate the breeding of resistant cultivars in wheat breeding programs.
Abstract: In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.
66 citations
TL;DR: The experimental results showed that the proposed robust method for wheatear detection based on the UAV platform in natural scenes can effectively mark the bounding of wheatears.
Abstract: In recent years, deep learning has greatly improved the ability of wheatear detection. However, there are still three main problems in wheatear detection based on unmanned aerial vehicle (UAV) platforms. First, dense wheat plants often overlap, and the wind direction will blur the pictures, which obviously interferes with the detection of wheatears; second, due to the different maturity, color, genotype, and head orientation, the appearance will also be different; third, UAV needs to take images in the field and conduct real-time detection, which requires the embedded module to detect wheatears quickly and accurately. Given the above problems, we studied and improved YoloV4, and proposed a robust method for wheatear detection using UAV in natural scenes. For the first problem, we modified the network structure, deleted the feature map with a size of $19\times 19$ , and used k-means algorithm to re-cluster the anchors, and we proposed a method of prediction box fusion. For the second problem, we used the pseudo-labeling method and data augmentation methods to improve the generalization ability of the model. For the third problem, we simplified the network structure, replaced the original network convolution with the improved depthwise separable convolution, and proposed an adaptive ReLU activation function to reduce the amount of calculation and speed up the calculation. The experimental results showed that our method can effectively mark the bounding of wheatears. In test sets, our method achieves 96.71% in f1-score, which is 9.61% higher than the state of the art method, and the detection speed is 23% faster than the original method. It can be concluded that our method can effectively solve the problems of wheatear detection based on the UAV platform in natural scenes.
19 citations
TL;DR: In this article, a new model using mobile video image processing and Long Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability.
Abstract: The disease infliction of the plants severely influences the yield. It alters the essence and extent of crop production cause fiscal distress. Consequently, the diagnosis of numerous plant diseases is significant to decrease the yield perdition by discovering crop infections at their earlier stages. This paper introduces a new model using mobile video image processing and Long-Short Term Memory (LSTM)-Simple Recurrent Neural Network (SRNN) deep learning method for the prediction of the diseased or disinfected rice plant with dynamic learning capability. The rice plant videos captured under uncontrolled conditions in day-lighting using a mobile handset and divided into two sections for the designing and testing of LSTM-SRNN models. After shooting, the video images of the rice plant segmented using colour indexing and linear color space transformation with minimal daylight impact. Low-level spatial features; entropy, standard deviation, and fuzzy features extracted after video image segmentation. The excerpted characteristics with the composite combinations transformed in time-series datasets with the desired response. The datasets employed in the LSTM-SRNN model for progressive learning. The distinct test video features applied in LSTM-SRNN to appraise the generalization capability of the proposed system with performance analysis. The experimental outcomes of the proposed LSTM-SRNN model exhibit 99.99% prediction ability with fuzzy features. The model also presents possibilities for dynamic learning adaptability and temporal information processing to overcome the limitations of many well-known rule-based and machine learning approaches.
16 citations
TL;DR: In this paper , an improved YoloV5 object detection network was employed to detect and record wheat ears in images collected from field plots at two locations over 2 years, which can be used as a quick, efficient, and convenient tool for assessment of the levels of damage caused by Fusarium head blight in wheat under field conditions.
16 citations
01 Aug 2021
TL;DR: In this article, a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers' community is presented.
Abstract: The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate.
12 citations
References
More filters
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Abstract: In this paper a theory of two-dimensional moment invariants for planar geometric figures is presented. A fundamental theorem is established to relate such moment invariants to the well-known algebraic invariants. Complete systems of moment invariants under translation, similitude and orthogonal transformations are derived. Some moment invariants under general two-dimensional linear transformations are also included. Both theoretical formulation and practical models of visual pattern recognition based upon these moment invariants are discussed. A simple simulation program together with its performance are also presented. It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished. It is also indicated that generalization is possible to include invariance with parallel projection.
7,963 citations
"Evaluation of Efficacy of Fungicide..." refers methods in this paper
...Hu [52] used central moment to construct seven invariant moments which can describe the region shape....
[...]
TL;DR: This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model.
Abstract: Summary 1 Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions 2 This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance) The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion 3 Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods 4 The unique features of BRT raise a number of practical issues in model fitting We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel ( Anguilla australis Richardson), a native freshwater fish of New Zealand We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data We provide code and a tutorial to enable the wider use of BRT by ecologists
4,787 citations
"Evaluation of Efficacy of Fungicide..." refers methods in this paper
...Based on the training set resampling and voting mechanism, random attribute selection is introduced in the training process of random forest classifier [49]....
[...]
TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
2,100 citations
"Evaluation of Efficacy of Fungicide..." refers background in this paper
...Compared to conventional algorithms of machine learning, although the accuracy of deep learning is normally high, it needs many more samples and much more time for model training [24]–[29]....
[...]
TL;DR: A review on deep learning methods for semantic segmentation applied to various application areas and points out a set of promising future works to help researchers decide which are the ones that best suit their needs and goals.
844 citations
"Evaluation of Efficacy of Fungicide..." refers methods in this paper
...Four metrics, Pixel Accuracy (PA), Precision, Recall and F-measure, were used to evaluate segmentation quality [56]–[58]....
[...]
TL;DR: A sustained, coordinated, and collaborative research program that was put in place shortly after the 1993 Fusarium head blight epidemic is summarized, a program intended to quickly lead to improved management strategies and outreach implementation and serves as a model to deal with other emerging plant disease threats.
Abstract: Wheat and barley are critical food and feed crops around the world. Wheat is grown on more land area worldwide than any other crop. In the United States, production of wheat and barley contributes to domestic food and feed use, and contributes to the export market and balance of trade. Fifteen years ago, Plant Disease published a feature article titled “Scab of wheat and barley: A re-emerging disease of devastating impact”. That article described the series of severe Fusarium head blight (FHB) epidemics that occurred in the United States and Canada, primarily from 1991 through 1996, with emphasis on the unparalleled economic and sociological impacts caused by the 1993 FHB epidemic in spring grains in the Northern Great Plains region. Earlier publications had dealt with the scope and damage caused by this disease in the United States, Canada, Europe, and China. Reviews published after 1997 further described this disease and its impact on North American grain production in the 1990s. This article r...
543 citations
"Evaluation of Efficacy of Fungicide..." refers background in this paper
...FHB can cause metamorphism of the whole wheat grains [6]...
[...]