Radial Basis Function Neural Network for the Segmentation of Plant leaf disease

Chouhan, Siddharth Singh; Kaul, Ajay; Singh, Uday Pratap

doi:10.1109/iscon47742.2019.9036299

Cited by 19 publications

(5 citation statements)

References 25 publications

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“…Penelitian mengenai identifikasi jenis manga menggunakan jaringan syaraf tiruan RBF, dimana pada penelitian ini model yang diterapkan dapat memperoleh nilai ketepatan identifikasi mencapai 100% [17]. Penelitian lainnya mengenai penerapan algoritma RBF dalam mengidentifikasi tanaman yang berpenyakit berdasarkan citra daunya, dimana pada penelitian algoritma RBF mampu melakukan segmentasi dengan akurasi yang lebih tinggi jika dibandingkan dengan metode lainnya [18]. Penelitian lainnya, tentang penerapan jaringan syaraf tiruan RBF yang digunakan untuk temu kembali citra atau untuk menemukan informasi yang terkandung pada citra, dimana pada penelitian ini model yang diusulkan memperoleh nilai akurasi tertinggi hingga 90,92% [19].…”

Section: Pendahuluanunclassified

Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun

Nuraini

Harlena

Amalya

et al. 2023

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Indonesia has vast forests, even ranked as the third largest forest in the world. However, currently many forest areas have been deforested or the phenomenon of losing tree cover and forest areas. Forest rehabilitation programs develop by prioritizing plant or tree species that have fast growth or are called fast growing species. However, many people do not know about these fast growing species. Even though knowledge about the types of fast growing plant species is very important for the community to have so that the community can find out which plants can accelerate forest rehabilitation. Fast growing species of plants can actually be identified from the shape of the leaves. This study aims to build a classification model for fast growing species plant images based on leaf images by applying the Radial Basis Function (RBF) artificial neural network algorithm with morphological feature extraction. Morphological feature extraction is used to identify the shape of an object in order to obtain feature values based on predetermined parameters. These features then become input for the RBF artificial neural network to obtain learning patterns. The RBF network has three layers that are feedforward so that it can support solving classification or pattern recognition problems. Based on the results of accuracy testing, an accuracy value of 87.50% was obtained. This means that the Radial Basis Function (RBF) neural network is able to classify fast growing plant species based on leaf images.

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Section: Pendahuluanunclassified

Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun

Nuraini

Harlena

Amalya

et al. 2023

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“…A region-expanding function was employed to extract image data, and bacterial foraging was optimized for the training of CNN. In the hidden layer of the ANN, a radial basis activation function was utilized [164]. Overall, 270 images in the PVD achieved a specificity of 85.58%.…”

Section: ) Rq7: In What Ways Do Conventional Image Processing and Mac...mentioning

confidence: 99%

A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

et al. 2023

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Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI), machine learning (ML), or deep learning (DL) methods and models to provide disease detection solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review (SLR) and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five major academic databases, namely Springer, IEEE Xplore, Scopus, Google Scholar, and ACM library. After deploying a comprehensive screening process, the review considered 176 final studies based on the importance of the method. Several crops, including grapes, rice, apples, cucumbers, maize, tomatoes, wheat, and potatoes, have tested mainly on the hyperspectral imagery and vision-centered approaches. Support Vector Machines (SVMs) and Logistic regression (LR) classifiers demonstrated an increased accuracy in experiments compared to traditional classifiers. Besides the image taxonomy, disease localization is depicted in these approaches as a bottle neck to disease detection. Cognitive CNNs with attention mechanisms and transfer learning are showing an increasing trend. There is no standard model performance assessment though the majority use accuracy, recall, precision, F1 Score, and confusion matrix. The available 11 datasets are laboratory and in-field based, and 9 are publicly available. Some laboratory-based datasets are considerably small, making them impractical in experiments. Finally, there is a need to avail models with fewer parameters, implementable on small devices and large datasets accommodating several crops and diseases to have robust models.INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.

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“…There is a clear need for efficient and accurate disease detection systems in order to enhance crop yields. To address this demand, technological advancements such as the integration of Artificial Intelligence (AI) have been employed for the purpose of detecting, classifying, and estimating the severity of crop diseases (Chouhan, Kaul, & Singh, 2019a ; Chouhan, Kaul, & Singh, 2019c ; Chouhan, Kaul, & Sinzlr, 2019 ). The existence of AI is a topic of significant interconnection with factors such as the accessibility of food, the environment, resilience, and climate variability, making it a crucial and influential instrument.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Ngugi,

Ezugwu,

Akinyelu

et al. 2024

Environ Monit Assess

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Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

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Radial Basis Function Neural Network for the Segmentation of Plant leaf disease

Cited by 19 publications

References 25 publications

Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun

Klasifikasi Jenis Tanaman Fast Growing Species Menggunakan Algoritma Radial Basis Function Berdasarkan Citra Daun

A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

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