Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach

One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T2 statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection. © 2010 Elsevier B.V. All rights reserved.This work has been supported by the Spanish Ministry of Education (MEC) and by European FEDER funds, through the research projects DPI2007-66596-C02-01 (VISTAC) and DPI-2007-66596-C02-02.López García, F.; Andreu García, G.; Blasco Ivars, J.; Aleixos Borrás, MN.; Valiente González, JM. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture. 71(2):189-197. doi:10.1016/j.compag.2010.02.001S18919771

Pixel classification methods for identifying and quantifying leaf surface injury from digital images

Plants exposed to stress due to pollution, disease or nutrient deficiency often develop visible symptoms on leaves such as spots, colour changes and necrotic regions. Early symptom detection is important for precision agriculture, environmental monitoring using bio-indicators and quality assessment of leafy vegetables. Leaf injury is usually assessed by visual inspection, which is labour-intensive and to a consid- erable extent subjective. In this study, methods for classifying individual pixels as healthy or injured from images of clover leaves exposed to the air pollutant ozone were tested and compared. RGB images of the leaves were acquired under controlled conditions in a laboratory using a standard digital SLR camera. Different feature vectors were extracted from the images by including different colour and texture (spa- tial) information. Four approaches to classification were evaluated: (1) Fit to a Pattern Multivariate Image Analysis (FPM) combined with T2 statistics (FPM-T2) or (2) Residual Sum of Squares statistics (FPM-RSS), (3) linear discriminant analysis (LDA) and (4) K-means clustering. The predicted leaf pixel classifications were trained from and compared to manually segmented images to evaluate classification performance. The LDA classifier outperformed the three other approaches in pixel identification with significantly higher accuracy, precision, true positive rate and F-score and significantly lower false positive rate and computation time. A feature vector of single pixel colour channel intensities was sufficient for capturing the information relevant for pixel identification. Including neighbourhood pixel information in the feature vector did not improve performance, but significantly increased the computation time. The LDA classifier was robust with 95% mean accuracy, 83% mean true positive rate and 2% mean false positive rate, indicating that it has potential for real-time applications.Opstad Kruse, OM.; Prats Montalbán, JM.; Indahl, UG.; Kvaal, K.; Ferrer Riquelme, AJ.; Futsaether, CM. (2014). Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and Electronics in Agriculture. 108:155-165. doi:10.1016/j.compag.2014.07.010S15516510

Table 2: Example applications of the use of remote sensing technologies to detect change in vegetation.

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops

Exploring the Viability of Gold Jewelry as a Diversifying and Safe-Haven Investment

The purpose of this paper is to analyze the potential benefits of the inclusion of gold jewelry in a traditional investment portfolio. A popular commodity investment choice, gold has been lauded for its various diversification and safe-haven characteristics. Popular investment forms include paper gold, gold bars and gold coins. However, gold jewelry, although officially categorized as a retail purchase, is often bought with the intention of investment as well. This paper examines the diversification benefits of gold jewelry by measuring the performance of a portfolio in which gold jewelry is present, using multiple performance measures to determine its risk and reward characteristics. A qualitative analysis of gold jewelry demand trends during specified economic periods is conducted to determine if gold jewelry exhibits the characteristics of a safe-haven asset. The paper concludes with an overall analysis of the benefits gold jewelry may provide to a portfolio, along with any risks an investor should be aware of

Exploring the Viability of Gold Jewelry as a Diversifying and Safe-Haven Investment

The purpose of this paper is to analyze the potential benefits of the inclusion of gold jewelry in a traditional investment portfolio. A popular commodity investment choice, gold has been lauded for its various diversification and safe-haven characteristics. Popular investment forms include paper gold, gold bars and gold coins. However, gold jewelry, although officially categorized as a retail purchase, is often bought with the intention of investment as well. This paper examines the diversification benefits of gold jewelry by measuring the performance of a portfolio in which gold jewelry is present, using multiple performance measures to determine its risk and reward characteristics. A qualitative analysis of gold jewelry demand trends during specified economic periods is conducted to determine if gold jewelry exhibits the characteristics of a safe-haven asset. The paper concludes with an overall analysis of the benefits gold jewelry may provide to a portfolio, along with any risks an investor should be aware of

STATISTICAL AND NEURAL NETWORK CLASSIFIERS FOR CITRUS DISEASE DETECTION USING MACHINE VISION

ABSTRACT. The citrus industry is an important constituent of Florida’s overall agricultural economy. Proper disease control measures must be undertaken in citrus groves to minimize losses. Technological strategies using machine vision and artificial intelligence are being investigated to achieve intelligent farming, including early detection of diseases in groves, selective fungicide application, etc. This research used a texture analysis method termed the color co-occurrence method (CCM) to determine whether classification algorithms could be used to identify diseased and normal citrus leaves. Normal and diseased citrus leaf samples with greasy spot, melanose, and scab were collected in the field and brought to the laboratory for the development of suitable segmentation and classification algorithms. Four feature models were created for classification analysis using varying subsets of a 39-variable texture feature set. The classification strategies used were based on a Mahalanobis minimum distance classifier, using the nearest neighbor principle, as well as neural network classifiers based on the back-propagation algorithm and radial basis functions. The leaf sample discriminant analysis using the Mahalanobis statistical classifier and the CCM textural analysis achieved classification accuracies of over 95 % for all classes (99 % mean accuracy) when using hue and saturation texture features. Likewise, a back-propagation neural network algorithm achieved accuracies of over 90 % for all classes (95 % mean accuracy) when using hue and saturation features. It was concluded that the Mahalanobis statistical classifier and the back-propagation neural network classifier performed equally well when using ten hue and saturation texture features selected through a stepwise variable reduction method. Future studies will seek to apply the developed algorithms in a natural citrus grove environment