Deep Learning-Based Object Detection of Barley Seeds

Computer vision techniques have been widely used in the food manufacturing industry today due to their speed, convenience, and low labor cost. As an important raw material in beer, barley seeds largely determine the flavor and taste of the beer brewing process. To ensure the quality of beer, beer brewers will strictly screen the varieties of barley seeds and ensure the purity of malt. Traditional manual detection needs a lot of professional training, and because of the high similarity between different kinds of barley seeds, tedious and time-consuming manual detection leads to a high error rate. However, chemical testing requires professional equipment, reagents, and laboratories, which have high-cost performance. Thus, an efficient and accurate object detection technique is considered to replace manually distinguishing barley seed types. This thesis uses the deep learning-based object detection network YOLOv3 model to help automatically and accurately detecting barley locations and identifying seed types from iPhone-based images of barley seeds. The barley seed samples used in this project are all provided by our industrial collaborator, and captured by iPhone 11 or iPhone 11 pro in high-definition resolutions. A total of nine varieties of barleys are trained in this study, and the data set includes images of a single grain and images of multiple grains (multi-categories). In this experiment, the best mAP (mean Average Precision) value we obtained is 97.1%, the model recognition (localization and classification) precision is 91.2%, and the recall rate is 95.9%

Automatic and fast classification of barley grains from images: A deep learning approach

Australia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use, as the intrinsic quality of the variety determines its market value. Manual identification of barley varieties by the naked eye is challenging and time-consuming for all stakeholders, including growers, grain handlers and traders. Current industrial methods for identifying barley varieties include molecular protein weights or DNA based technology, which are not only time-consuming and costly but need specific laboratory equipment. On grain receival, there is a need for efficient and low-cost solutions for barley classification to ensure accurate and effective variety segregation. This paper proposes an efficient deep learning-based technique that can classify barley varieties from RGB images. Our proposed technique takes only four milliseconds to classify an RGB image. The proposed technique outperforms the baseline method and achieves a barley classification accuracy of 94% across 14 commercial barley varieties (some highly genetically related)

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods

Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

Computer vision classification of barley flour based on spatial pyramid partition ensemble

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples' classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification1913CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ420562/2018-

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system.  This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons

Weed control in organic cereals and pulses

Weeds remain one of the most significant agronomic problems associated with organic arable crop production. It is recognised that a low weed population can be beneficial to the crop as it provides food and habitat for a range of beneficial organisms (Millington et al., 1990; Clements et al., 1994; Aebischer, 1997; Fuller, 1997; Patriquin et al., 1998). However, above critical population thresholds, weeds can significantly reduce crop yield and quality in conventional (Cussans, 1968; Hewson et al., 1973; Cousens, 1985; Cudney et al., 1989) and organic (Bulson, 1991) crops alike. The challenge for organic farmers is to manage weeds in such a way as to accommodate their beneficial effects whilst still producing an acceptable crop

Rice seed image classification based on HOG descriptor with missing values imputation

Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists’ scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications

DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018