Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset

Xiong, Yan; Liang, Longfei; Wang, Lin; She, Jinhua; Wu, Min

doi:10.1016/j.compag.2020.105712

Cited by 88 publications

(33 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Xiong et al [88] proposed an automatic image segmentation algorithm based on the GrabCut algorithm and selected the MobileNet as DL classification model, and designed a crop disease recognition system for mobile smart devices. The system had a recognition accuracy of more than 80% for a total of 27 diseases of 6 crops in the laboratory environment and the field.…”

Section: The System Of Leaf-disease Detectionmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

417

View full text Add to dashboard Cite

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

show abstract

Section: The System Of Leaf-disease Detectionmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

417

View full text Add to dashboard Cite

show abstract

“…e remaining points are judged as uncontaminated pixels. If they are judged as noisy, they are filtered; if they are judged as non-noisy, they are not processed and are directly output as signal points to achieve the purpose of selectively processing grayscale images and retaining details while denoting [19][20][21][22][23][24]. Finally, the two-dimensional OTSU algorithm operation is performed on the grayscale image to complete the segmentation.…”

Section: Adaptive and Fast Algorithm Researchmentioning

confidence: 99%

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

Chai

2021

Complexity

View full text Add to dashboard Cite

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

show abstract

“…When the type of disease changes, the accuracy of disease classification is reduced. Nowadays, deep learning (DL) algorithms, especially those based on convolutional neural networks (CNNs), which is a subset of DL, are widely used in plant disease classification tasks [23][24][25][26]. In our previous work [22], we studied spectral and image data reduction methods for multidiseased leaves with similar symptoms regardless of the plant variety.…”

Section: Introductionmentioning

confidence: 99%

Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Tan

Jiang

2021

AgriEngineering

View full text Add to dashboard Cite

Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) have developed and examined various traditional machine learning (ML) and deep learning (DL) algorithms for plant disease classification. However, through pass survey analysis, we found that there are no studies comparing the classification performance of ML and DL for the tomato disease classification problem. The performance and outcomes of different traditional ML and DL (a subset of ML) methods may vary depending on the datasets used and the tasks to be solved. This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem. For machine learning algorithm implementation, we used different methods to extract disease features manually. In our study, we extracted a total of 52 texture features using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) methods and 105 color features using color moment and color histogram methods. Among all the feature extraction methods, the COLOR+GLCM method obtained the best result. By comparing the different methods, we found that the metrics (accuracy, precision, recall, F1 score) of the tested deep learning networks (AlexNet, VGG16, ResNet34, EfficientNet-b0, and MobileNetV2) were all better than those of the measured machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF)). Furthermore, we found that, for our dataset and classification task, among the tested ML/DL algorithms, the ResNet34 network obtained the best results, with accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and F1 score of 99.7%.

show abstract

Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset

Cited by 88 publications

References 14 publications

Plant Disease Detection and Classification by Deep Learning—A Review

Plant Disease Detection and Classification by Deep Learning—A Review

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Contact Info

Product

Resources

About