Quality grading of soybean seeds using image analysis

Image processing and machine learning technique are modified to use the quality grading of soybean seeds. Due to quality grading is a very important process for the soybean industry and soybean farmers. There are still some critical problems that need to be overcome. Therefore, the key contributions of this paper are first, a method to eliminate shadow noise for segment soybean seeds of high quality. Second, a novel approach for color feature which robust for illumination changes to reduces problem of color difference. Third, an approach to discover a set of feature and to form classifier model to strengthen the discrimination power for soybean classification. This study used background subtraction to reduce shadow appearing in the captured image and proposed a method to extract color feature based on robustness for illumination changes which was H components in HSI model. We proposed classifier model using combination of the color histogram of H components in HSI model and GLCM statistics to represent the color and texture features to strengthen the discrimination power of soybean grading and to solve shape variance in each soybean seeds class. SVM classifiers are generated to identify normal seeds, purple seeds, green seeds, wrinkled seeds, and other seed types. We conducted experiments on a dataset composed of 1,320 soybean seeds and 6,600 seed images with varies in brightness levels. The experimental results achieved accuracies of 99.2%, 97.9%, 100%, 100%, 98.1%, and 100% for overall seeds, normal seeds, purple seeds, green seeds, wrinkled seeds, and other seeds, respectivel

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201

Analysis and prediction of seed quality using machine learning

The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei