A New Segmentation method for Plant Disease Diagnosis

Gurrala, Kiran Kumar; Yemineni, Lenin; Rayana, Krupa Spandan Raj; Vajja, Lokesh Kumar

doi:10.1109/icct46177.2019.8969021

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Several machine learning approaches, such as artificial neural network (ANN), decision tree, k-mean, k nearest neighbor, and support vector machine (SVM) have been explored for a range of agricultural researches (Mucherino et al, 2009;Rumpf et al, 2010). Amongst all, the SVM has been the most investigated for the detection of plant diseases (Araujo and Peixoto, 2019;Bukka et al, 2020;Gurrala et al, 2019;Sood and Singh, 2020). The literature review relevant to conventional machine learning approaches is summarized in Table 1.…”

Section: Conventional Machine Learning Algorithms For the Diagnosis O...mentioning

confidence: 99%

“…Authors have used different features extraction techniques (such as color moments technique for color features, local binary patterns for texture features, speeded up robust features, and a bag of visual words algorithms for local features) coupled with SVM classification, with an accuracy of 75.8 % for soybean plant disease identification. Gurrala et al (2019) proposed an image segmentation technique termed modified color processing detection algorithm followed by gray level co-occurrence matrix (GLCM) to extract features from 100 diseased leaves to identify the diseases like anthracnose, leaf spot, leaf blight, and scab. The author has used SVM to classify these diseases, and the results show that the proposed segmentation approach was more efficient and accurate compared to k-mean clustering segmentation.…”

Section: Conventional Machine Learning Algorithms For the Diagnosis O...mentioning

confidence: 99%

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Plant disease diagnosis using image processing techniques -A review on machine and deep learning approaches

Patel¹,

Patel²

2022

EEC

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Diagnosis and monitoring of plant diseases at early plant growth stages are crucial to minimizing the disease dissemination and it also facilitates effective plant protection measures. Thus, the automatic and real-time system for the diagnosis of plant diseases is very much needed in the current era of agricultural information. Several techniques and/or approaches of image processing have been studied to address the challenges in the diagnosis of plant diseases through acquired images. Of them, conventional machine learning and advanced deep learning (particularly CNN) approaches are nowadays explored considerably and have demonstrated promising and relatively an accurate classification than those of conventional approaches. The present literature review aims to present and discuss the potential applications of these techniques.

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Section: Conventional Machine Learning Algorithms For the Diagnosis O...mentioning

confidence: 99%

Section: Conventional Machine Learning Algorithms For the Diagnosis O...mentioning

confidence: 99%

Plant disease diagnosis using image processing techniques -A review on machine and deep learning approaches

Patel¹,

Patel²

2022

EEC

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“…Unsupervised-learning-based methods [15,16] do not require any labeled data during training, so they are more suitable when it is difficult to collect labeled data [17][18][19]. Furthermore, they can eliminate labeling error [32] and can be trained on relatively larger datasets than those used in supervised learning.…”

Section: Anomaly Detection 221 Unsupervised Anomaly Detectionmentioning

confidence: 99%

“…For example, there are numerous types of plant diseases, such as mildew, wilt, mosaic virus, and rust, and their shapes vary greatly depending on circumstances. In addition, ambiguous boundaries of diseased areas on plants and long growth cycles render plant disease detection even more challenging [17][18][19]. Even if a dataset considering all these cases is acquired, supervised-learning-based CNN models tend to be overfitted to a dominant class due to the class imbalance problem [20,21] between class categories, which deteriorates the ability of feature presentation and may result in redundant performance for classes with relatively fewer training data than that of the dominant class.…”

Section: Introductionmentioning

confidence: 99%

Instance-Aware Plant Disease Detection by Utilizing Saliency Map and Self-Supervised Pre-Training

Kim

Baik³

et al. 2022

Agriculture

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Plant disease detection is essential for optimizing agricultural productivity and crop quality. With the recent advent of deep learning and large-scale plant disease datasets, many studies have shown high performance of supervised learning-based plant disease detectors. However, these studies still have limitations due to two aspects. First, labeling cost and class imbalance problems remain challenging in supervised learning-based methods. Second, plant disease datasets are either unstructured or weakly-unstructured and the shapes of leaves and diseased areas on them are variable, rendering plant disease detection even more challenging. To overcome these limitations, we propose an instance-aware unsupervised plant disease detector, which leverages normalizing flows, a visual saliency map and positional encodings. A novel way to explicitly combine these methods is the proposed model, in which the focus is on reducing background noise. In addition, to better fit the model to the plant disease detection domain and to enhance feature representation, a feature extractor is pre-trained in a self-supervised learning manner using only unlabeled data. In our extensive experiments, it is shown that the proposed approach achieves state-of-the-art performance on widely-used datasets, such as BRACOL (Weakly-unstructured) and PlantVillage (Unstructured), regardless of whether the dataset is weakly-structured or unstructured.

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“…Note that the extracted texture features are the most relevant and most useful for representing the disease-affected regions in the images, which are then employed to train the support vector machine (SVM) and neural network (NN) classifier. It is further emphasized that these texture features are arithmetical parameters that are automatically calculated by means of the gray level co-occurrence matrix (GLCM) [64,65], as stated below:…”

Section: Comparison Of Various Crop Disease Detection Techniquesmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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A New Segmentation method for Plant Disease Diagnosis

Cited by 9 publications

References 7 publications

Plant disease diagnosis using image processing techniques -A review on machine and deep learning approaches

Plant disease diagnosis using image processing techniques -A review on machine and deep learning approaches

Instance-Aware Plant Disease Detection by Utilizing Saliency Map and Self-Supervised Pre-Training

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

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