Data Mining - Concepts and Techniques.

Anthracnose, brown spot, mites, black rot, downy mildew, and leaf blight are six common grape leaf pests and diseases, which cause severe economic losses to the grape industry. Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. This paper proposes a novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases. First, based on 4,023 images collected in the field and 3,646 images collected from public data sets, a data set of 107,366 grape leaf images is generated via image enhancement techniques. Afterward, Inception structure is applied for strengthening the performance of multi-dimensional feature extraction. In addition, a dense connectivity strategy is introduced to encourage feature reuse and strengthen feature propagation. Ultimately, a novel CNN-based model, namely, DICNN, is built and trained from scratch. It realizes an overall accuracy of 97.22% under the hold-out test set. Compared to GoogLeNet and ResNet-34, the recognition accuracy increases by 2.97% and 2.55%, respectively. The experimental results demonstrate that the proposed model can efficiently recognize grape leaf diseases. Meanwhile, this study explores a new approach for the rapid and accurate diagnosis of plant diseases that establishes a theoretical foundation for the application of deep learning in the field of agricultural information.

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Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks.

Most of the grape plant diseases are severe and spread rapidly. The grape plant diseases start on the leaf and then spread to stem, fruit, and root. It has problems with time-consuming and lacking knowledge for farmers in remote areas to classify grapes leaf diseases because of limited access to human experts in developing countries. In recent research, grape leaf diseases are classified with fine-tuning VGG16 Network. But the classification was not received good accuracy results with VGG16 Architecture. This system proposes transfer learning via fine-tuning of VGG16 network, one of CNN Architecture, to classify diseases on grape leaf The system used Global Average Pooling (GAP) layer instead of VGG16's two fully connected layers before final classification SoftMax layer to improve accuracy result of fine-tuning VGG16 for grape leaf diseases classification. The proposed system mainly analyzed healthy leaves and five leaves diseases, named anthracnose, downy mildew, black measles, isariopsis leaf spot, nutrient insufficient, on 6000 images dataset of Myanmar Grapevine Yard. The proposed system compared the accuracy with VGG16 using fully connected layers and VGG16 using SVM classifier. The proposed system outperformed with 98.4% accuracy than others.

Grape Leaf Diseases Classification using Convolutional Neural Network

Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.

Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks

The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.

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Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning.

Grapes (Vitis Vinifera) is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a Deep Learning model named Convolutional Neural Network. Feature extraction and model training of the leaf images is performed using pre-defined AlexNet architecture. The image Dataset is taken from “National Research Centre for Grapes” (ICAR). It consists of images of diseases named Powdery mildew, Downy mildew, Rust, Bacterial Spots and Anthracnose. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is 98.23% for powdery mildew vs bacterial spots.

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Grapes Leaf Disease Detection using Convolutional Neural Network

Information regarding forest land is highly required for developing ecosystem management. This paper provides an analysis related to classification and prediction estimation using machine learning techniques. The approach is to predict the forest cover type using the cartographic variables like aspect, slope, soil type, wilderness area etc. Various Data mining techniques such as decision tress, random forest, regression trees, and gradient boosting machines are used for prediction of the forest cover type. Using these machine learning methods models have been developed and tested for accuracy ranging from 19.4% to 74.8%. Kaggle dataset which is the standard benchmarking dataset, is taken for comparison studies. The comparisons of these models are done to identify a better model for predicting the forest cover type with better accuracy. For performance comparison, metrics like accuracy and error rate are used. An important aspect of the study is the use of different performance measures to evaluate the learning methods.

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Tanmay Anant Wagh

Papers

Grapes Leaf Disease Detection using Convolutional Neural Network

Forest Cover Type Prediction using Cartographic Variables