Quantifying soybean phenotypes using UAV imagery and machine learning, deep learning methods

Crop breeding programs aim to introduce new cultivars to the world with improved traits to solve the food crisis. Food production should need to be twice of current growth rate to feed the increasing number of people by 2050. Soybean is one the major grain in the world and only US contributes around 35 percent of world soybean production. To increase soybean production, breeders still rely on conventional breeding strategy, which is mainly a 'trial and error' process. These constraints limit the expected progress of the crop breeding program. The goal was to quantify the soybean phenotypes of plant lodging and pubescence color using UAV-based imagery and advanced machine learning. Plant lodging and soybean pubescence color are two of the most important phenotypes for soybean breeding programs. Soybean lodging and pubescence color is conventionally evaluated visually by breeders, which is time-consuming and subjective to human errors. The goal of this study was to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in the assessment of lodging conditions and deep learning in the assessment pubescence color of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1,266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores and pubescence scores were visually assessed by experienced breeders. Lodging scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. In contrast, pubescence color scores were grouped into three classes, i.e., gray, tawny, and segregation. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN), and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data pre-processing methods were used to treat the imbalanced dataset to improve the classification accuracy. Results indicate that the pre-processing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Over-sampling-Edited Nearest Neighbor (SMOTE-ENN) may be an excellent pre-processing method for using unbalanced datasets and classification tasks. Furthermore, an overall accuracy of 96 percent was obtained using the SMOTE-ENN dataset and ANN classifier. On the other hand, to classify the soybean pubescence color, seven pre-trained deep learning models, i.e., DenseNet121, DenseNet169, DenseNet201, ResNet50, InceptionResNet-V2, Inception-V3, and EfficientNet were used, and images of each plot were fed into the model. Data was enhanced using two rotational and two scaling factors to increase the datasets. Among the seven pre-trained deep learning models, ResNet50 and DenseNet121 classifiers showed a higher overall accuracy of 88 percent, along with higher precision, recall, and F1-score for all three classes of pubescence color. In conclusion, the developed UAV-based high-throughput phenotyping system can gather image features to estimate soybean crucial phenotypes and classify the phenotypes, which will help the breeders in phenotypic variations in breeding trials. Also, the RGB imagery-based classification could be a cost-effective choice for breeders and associated researchers for plant breeding programs in identifying superior genotypes.Includes bibliographical references

Analysis of color and texture characteristics of cereals on digital images

The color of the grain shell of cereals is an important feature that characterizes the pigments and metabolites contained in it. The grain shell is the main barrier between the grain and the environment, so its characteristics are associated with a number of important biological functions: moisture absorption, grain viability, resistance to pre-harvest germination. The presence of pigments in the shell affects various technological properties of the grain. Color characteristics, as well as the appearance of the grain shell are an important indicator of plant diseases. In addition, the color of the grains serves as a classifying feature of plants. Genetic control of the color formation of both grains and other plant organs is exerted by genes encoding enzymes involved in the biosynthesis of pigments, as well as regulatory genes. For a number of pigments, these genes are well understood, but for some pigments, such as melanin, which causes the black color of grains in barley, the molecular mechanisms of biosynthesis are still poorly understood. When studying the mechanisms of genetic control of grain color, breeders and geneticists are constantly faced with the need to assess the color characteristics of their shell. The technical means of addressing this problem include spectrophotometers, spectrometers, hyperspectral cameras. However, these cameras are expensive, especially with high resolution, both spatial and spectral. An alternative is to use digital cameras that allow you to get high-quality images with high spatial and color resolution. In this regard, recently, in the field of plant phenotyping, methods for evaluating the color and texture characteristics of cereals based on the analysis of two-dimensional images obtained by digital cameras have been intensively developed. This mini-review is devoted to the main tasks related to the analysis of color and texture characteristics of cereals, and to methods of their description based on digital images

Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods

Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network as the generator and encoder with Capsule Network as the discriminator while experimenting on images with random composition and translation inferences. Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers