Precision Farming
F. Fatehi; H. Bagherpour; J. Amiri Parian
Abstract
Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and ...
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Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and precise capabilities of deep convolutional neural networks (DCNN), the objective of this study is to investigate the effectiveness of the optimized YOLOv8s model in detecting bloomed Damask roses. To assess the impact of the YOLO model size on network performance, the precision and detection speed of other YOLO network versions, including v5s and v6s, were also examined. Images of Damask roses were taken under two lighting conditions: normal light conditions (from civil twilight to sunrise) and intense light conditions (from sunrise to 10 AM). The outcomes demonstrated that YOLOv8s exhibited the highest performance, with a mean average precision (mAP50) of 98% and a detection speed of 243.9 fps. This outperformed the mAP50 and detection speed of YOLOv5s and YOLOv6s networks by margins of 0.3%, 6.1%, 169.3 fps and 198.6 fps, respectively. Experimental results show that YOLOv8s performs better on images taken in normal lighting than on those taken in intense lighting. A decline of 5.2% in mAP50 and 2.4% in detection speed signifies the adverse influence of intense ambient light on the model's effectiveness. This research indicates that the real-time detector YOLOv8s provides a feasible solution for the identification of Damask rose and provides guidance for the detection of other similar plants.
Image Processing
H. Bagherpour; F. Fatehi; A. Shojaeian; R. Bagherpour
Abstract
In some countries, people commonly consume hazelnuts in their shells to extend shelf life or due to technological limitations. Therefore, open-shell hazelnuts are more marketable. At the semi-industrial scale, open-shell and closed-shell hazelnuts are currently separated from each other through visual ...
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In some countries, people commonly consume hazelnuts in their shells to extend shelf life or due to technological limitations. Therefore, open-shell hazelnuts are more marketable. At the semi-industrial scale, open-shell and closed-shell hazelnuts are currently separated from each other through visual inspection. This study aims to develop a new algorithm to separate open-shell hazelnuts from cracked or closed-shell hazelnuts. In the first approach, dimension reduction techniques such as Sequential Forward Feature Selection (SFFS) and Principal Component Analysis (PCA) were used to select or extract a combination of color, texture, and grayscale features for the model’s input. In the second approach, individual features were used directly as inputs. In this study, three famous machine learning models, including Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-Layer Perceptron (MLP) were employed. The results indicated that the SFFS method had a greater effect on improving the performance of the models than the PCA method. However, there was no significant difference between the performance of the models developed with combined features (98.00%) and that of the models using individual features (98.67%). The overall results of this study indicated that the MLP model, with one hidden layer, a dropout of 0.3, and 10 neurons using Histogram of Oriented Gradients (HOG) features as input, is a good choice for classifying hazelnuts into two classes of open-shell and closed-shell.
Precision Farming
A. Naderi Beni; H. Bagherpour; J. Amiri Parian
Abstract
IntroductionDetection of tree leaf diseases plays a crucial role in the horticultural field. These diseases can originate from viruses, bacteria, fungi, and other pathogens. If proper attention is not given, these diseases can drastically affect trees, reducing both the quality and quantity of yields. ...
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IntroductionDetection of tree leaf diseases plays a crucial role in the horticultural field. These diseases can originate from viruses, bacteria, fungi, and other pathogens. If proper attention is not given, these diseases can drastically affect trees, reducing both the quality and quantity of yields. Due to the importance of quince in Iran's export market, its diseases can cause significant economic losses to the country. Therefore, if leaf diseases can be automatically identified, appropriate actions can be taken in advance to mitigate these losses. Traditionally, the identification and detection of tree diseases rely on experts' naked-eye observations. However, the physical condition of the expert such as eyesight, fatigue, and work pressure can affect their decision-making capability. Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. This approach has significantly enhanced the classification and identification of diseases affecting plants and trees. This study employs a novel CNN algorithm alongside two pre-trained models to effectively identify and classify various types of quince diseases.Materials and MethodsImages of healthy and diseased leaves were acquired from several databases. The majority of these images were sourced from the Agricultural Research Center of Isfahan Province in Iran, supplemented by contributions from researchers who had previously studied in this field. Other supporting datasets were obtained from internet sources. This study incorporated a total of 1,600 images, which included 390 images of fire blight, 384 images of leaf blight, 406 images of powdery mildew, and 420 images of healthy leaves. Of all the images obtained, 70%, 20%, and 10% were randomly selected for the network's training, validation, and testing, respectively. Image flipping, rotation, and zooming were applied to augment the training dataset. In this research, a proposed convolutional neural network (CNN) combined with image processing was developed to classify quince leaf diseases into four distinct classes. Three CNN models, including Inception-ResNet-v2, ResNet-101, and our proposed CNN model, were investigated, and their performances were compared using essential indices including precision, sensitivity, F1-score, and accuracy. To optimize the models’ performance, the impact of dropout with a 50% probability and the number of neurons in the hidden layers were examined. Our proposed CNN model consists of an architecture with four convolutional layers, with 224 × 224 RGB images as input to the first layer, which has 16 filters, followed by additional convolutional layers with 32, 64, and 128 filters respectively. Activation functions of ReLU combined with max-pooling were used at each convolutional layer, and Softmax activation was applied in the last layer of the neural network to convert the output into a probability distribution.Results and DiscussionThree confusion matrices based on the test dataset were constructed for all the CNN models to compare and evaluate the performance of the classifiers. The indices obtained from the confusion matrices indicated that Inception-ResNet-v2 and ResNet-101 achieved accuracies of 79% and 72%, respectively. While all models exhibited promising efficiency in classifying leaf diseases, the proposed shallow CNN model stood out with an impressive accuracy of 91%, marking it as the most effective solution. The comprehensive results indicate that the optimized CNN model, featuring four convolutional layers, one hidden layer with 64 neurons, and a dropout rate of 0.5, outperformed the transfer learning models.ConclusionThe findings of this study demonstrate that our developed proposed CNN model provides a high-performance solution for the rapid identification of quince leaf diseases. It excels in real-time detection and monitoring, achieving remarkable accuracy. Notably, it can identify fire blight and powdery mildew with a precision exceeding 95%.
Precision Farming
M. Hashemi Jozani; H. Bagherpour; J. Hamzei
Abstract
Fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) are the most important indicators of greenness and have a strong correlation with green biomass. The objective of this study was to evaluate a hand-held GreesnSeeker (GS) active remote sensing instrument to estimate NDVI ...
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Fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) are the most important indicators of greenness and have a strong correlation with green biomass. The objective of this study was to evaluate a hand-held GreesnSeeker (GS) active remote sensing instrument to estimate NDVI and FVC in the spinach plant. In this study, the color indices of the G-B index and Excess Green (ExG) were used as color vegetation indices to discriminate leaves from soil background. During 28 to 44 days after emergence (DAG), the results showed good correlations between chlorophyll yield and NDVI (R = 0.61 to 0.91), and the correlation between NDVI of GS and biomass was significant. In addition, in this growth stage, the results showed a good coefficient of correlation between NDVI of GS and FVC (R = 0.67 to 0.82). In assessing the nitrogen rate on the NDVI of GS, the results showed significant differences only at the short period of growth stage (28 to 36 DAG). The results revealed that GreenSeeker performed well for estimation both chlorophyll and biomass yield of spinach crop and it could be used as a suitable instrument for estimation of leaf area index in the middle of the plant growth period.
