Image Processing
H. Koroshi Talab; D. Mohammad Zamani; M. Gholami Parashkoohi
Abstract
Diagnosing plant diseases is an important part of crop management and can significantly affect the quantity and quality of production. Traditional methods of visual assessment by human observers are time-consuming, costly, and error-prone, making accurate diagnosis and differentiation between various ...
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Diagnosing plant diseases is an important part of crop management and can significantly affect the quantity and quality of production. Traditional methods of visual assessment by human observers are time-consuming, costly, and error-prone, making accurate diagnosis and differentiation between various diseases difficult. Advances in agriculture have enabled the use of non-destructive machine vision systems for plant disease detection, and color imaging sensors have demonstrated great potential in this field. This study presents a framework for diagnosing early blight and late blight diseases in potatoes using a combination of Relief feature selection algorithms and Random Forest classification, along with color, texture, and shape features in three color spaces: RGB, HSV, and CIELAB (Lab*). The results indicated that the diagnostic accuracy for the early blight and late blight disease groups, as well as the healthy leaf group, were 94.71%, 95%, and 95.2%, respectively. The overall accuracy for disease classification was 95.99%. Additionally, the diagnostic accuracy for the early blight and late blight disease groups, along with the healthy leaf group, was 91.07%, 98.36%, and 98.93%, respectively, with an overall classification accuracy of 96.12%. After separating the diseased area from the healthy part of the leaf, a total of 150 features were extracted, including 45 color, 99 textural, and 6 shape features. The most effective features for disease detection were identified using a combination of all three feature sets. This study demonstrated that integrating these three sets of features can lead to a more accurate classification of potato leaves and provide valuable insights into the diagnosis and classification of potato diseases. This approach can help farmers and other plant disease specialists to accurately diagnose and manage potato diseases, and ultimately lead to an increase in product quality and yield.
Image Processing
A. Jahanbakhshi; K. Kheiralipour
Abstract
Introduction Carrot is one of the most important agricultural products used by millions of people all over the world. Quality assessment of agricultural products is one of the most important factors in improving the marketability of agricultural products. In the market, carrots with irregular shapes ...
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Introduction Carrot is one of the most important agricultural products used by millions of people all over the world. Quality assessment of agricultural products is one of the most important factors in improving the marketability of agricultural products. In the market, carrots with irregular shapes are not commonly picked by customers due to their appearance. This causes to remain those carrots in the market for a long time and then decay. Therefore, adopting an appropriate method for sorting and packaging this product can increase its desirability in the market. Packaging and sorting of carrots by workers bring about many problems such as high cost, product waste, etc. Image processing systems are modern methods which have different applications in agriculture including sorting of different products. The aim of this study was to implement a machine vision system to classify carrot based on their shape using image processing. Materials and Methods In this study, 135 carrot samples with different shapes (56 regulars and 79 irregulars) were selected and their images were obtained through an imaging system. First, an expert divided the carrots into, two categories according to their shapes. The carrots which had irregular shape were those with double or triple roots, cracked carrots, curved carrots, damaged carrots, and broken ones and those with upright shapes were considered as regular shape carrot. After imaging, image processing was started by an algorithm programmed in Matlab R2012a medium. Then some shape features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid non-homogeneity, and width non-homogeneity were extracted. After the selection of efficient features, artificial neural networks and support vector machine were used to classify the efficient features. Results and Discussion The number of neurons in the hidden layers of artificial neural network models were varied to find the optimal model. The highest percentage of the correct classification rate (98.50%) belonged to the structure of 2-10-16, which in fact has 16 neurons in the input layer, 10 neurons in the hidden layer and 2 neurons in the output layer. This model has also the lowest mean squared error and the highest correlation coefficient of the test data, 0.90 and 2.52, respectively. This network was a feed forward back propagation error type and the activation functions in hidden and output layers were Tansig and Perlin, respectively. The correct classification rate of the support vector machine method was 89.62%. The confusion matrix of support vector machine method showed that out of 56 usual samples, 42 specimens were correctly identified but 14 samples were mistakenly classified as unusual carrots. All 79 carrots with unusual shapes were correctly classified. The results obtained from the comparison of the performance of the two methods, the neural network method has a good superiority than the support vector machine for classification. Conclusion In this research, the classification of carrots was based on its appearance. At first the physical characteristics and appearance attributes of the carrot samples were extracted and processed using image processing. Image analysis was included the classification of samples into two usual and unusual shapes, which to classify the extracted properties two methods were used: the artificial neural network (ANN) and support vector machine (SVM). The classification accuracy of the ANN method was higher than SVM. It can be said that the image processing method can be used to improve the traditional method for grading the carrot product in new ways. So, the marketability of the product will be increased, and thus its losses will be reduced. Also, the image processing technique can be used as a simple, fast and non-destructive alternative to other methods of extracting geometric properties of agricultural products. Finally, it can be stated that image processing method and machine vision are effective ways for improving the traditional sorting techniques for carrots.
