Abstract:Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is c… Show more
“…With the development of computer vision technology, pattern recognition has been widely used in plant disease diagnosis [5]. The traditional plant disease diagnosis methods include features extraction and analysis [6].…”
The output and quality of apples were greatly threatened by plant diseases. Identifying the types and grades of diseases in time was helpful to the management of diseases. When the disease occurs on a leaf, there was little change in the leaf except for the affected area. The traditional attention mechanism changed the weight of the network to all pixels in the image, which affected the ability of the network to extract the features of the lesion area. And the traditional method of data enhancement was easy to cause local similarity and local discontinuity of all sample features in the same disease grade. In this paper, the improved metric matrix for kernel regression (IMMKR) was used to reduce the influence of local similarity and local discontinuity of all sample features in the same class. Then, a new attention mechanism by fusing the lesion location based on visual features was proposed, and the attention of the model to the lesion area was strengthened. The experiments were carried out on three different diseases of apple named black rot, scab, and rust. The accuracy rate and recall rate of the new method on the combined dataset of PlantVillage and PlantDoc were 91.55% and 92.06%, respectively, which was superior to existing methods. This algorithm has important reference significance for the identification and promotion of crop diseases.
“…With the development of computer vision technology, pattern recognition has been widely used in plant disease diagnosis [5]. The traditional plant disease diagnosis methods include features extraction and analysis [6].…”
The output and quality of apples were greatly threatened by plant diseases. Identifying the types and grades of diseases in time was helpful to the management of diseases. When the disease occurs on a leaf, there was little change in the leaf except for the affected area. The traditional attention mechanism changed the weight of the network to all pixels in the image, which affected the ability of the network to extract the features of the lesion area. And the traditional method of data enhancement was easy to cause local similarity and local discontinuity of all sample features in the same disease grade. In this paper, the improved metric matrix for kernel regression (IMMKR) was used to reduce the influence of local similarity and local discontinuity of all sample features in the same class. Then, a new attention mechanism by fusing the lesion location based on visual features was proposed, and the attention of the model to the lesion area was strengthened. The experiments were carried out on three different diseases of apple named black rot, scab, and rust. The accuracy rate and recall rate of the new method on the combined dataset of PlantVillage and PlantDoc were 91.55% and 92.06%, respectively, which was superior to existing methods. This algorithm has important reference significance for the identification and promotion of crop diseases.
“…There are more than a dozen different diseases of tomato plants in practice, so it is essential to detect them as accurately and as early as possible to prevent and treat the disease [3,4]. There are different methods of detecting plant diseases for different treatment, including those that use artificial intelligence (AI) algorithms of SVM based on image futures or neural networks (NNs) [5][6][7]. It is obvious that AI techniques have been applied in many fields of agriculture to identify plant diseases such as apple, tomato, and rice; others have used deep learning networks, which will be applied in this research for classifying tomato leaf diseases [8][9][10][11].…”
Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets.
“…In our opinion, such adaptive systems have a serious drawback, they cannot be customized to the individual characteristics of each root crop. In digital agriculture [10,11], computer vision systems are used to quickly detect and count plants [12][13][14][15], to determine their ripeness and diseases [16][17][18][19][20], as part of systems to protect against weeds and pests [21,22], to determine the position of cattle [23]. In recent years publications have shown that the problem of identifying diseased or mechanically damaged fetuses on transportation systems such as conveyor belts, drums, turbines and etc.…”
Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu's binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 × 416 pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which ranged from 97% to 100%, depending on the damage class. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy ranged from 90% to 95% depending on the root crops damage class.
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