Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks

Meeradevi,; Ranjana,; Mundada, Monica R; Sawkar, Soumya P; Bellad, Rithika S; Keerthi, Poornima S

doi:10.1109/discover50404.2020.9278067

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…Where manual analysis may result in the wrong treatment of crops and the wrong usage of chemicals if not done by an experienced and expert person. Classification is finally the predicament where we start labeling or classifying images based on the basic extracted features [59], [60]. The use of deep learning with image processing has come out as a splendid combination to apply in such relative fields.…”

Section: Classificationmentioning

confidence: 99%

Techniques of deep learning and image processing in plant leaf disease detection: a review

Kini

Prema

Pai

2023

IJECE

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<span lang="EN-US">Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold <span>the ophthalmic functionality of the human eye. In agriculture, leaf examination</span> is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.</span>

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Section: Classificationmentioning

confidence: 99%

Techniques of deep learning and image processing in plant leaf disease detection: a review

Kini

Prema

Pai

2023

IJECE

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“…• Anchors Anchors are bounding boxes of different sizes and ratios that are predefined and used as a reference for object detection using RPN. Anchor boxes are mainly used to find the scale and aspect ratio of objects required to be detected and it depends on the sizes of objects in the training set [26]- [28]. Originally, there are 3 scales and 3 aspect ratios which makes k=9.…”

Section: • Convolutional Layermentioning

confidence: 99%

Plant Disease Classification using Alex Net

Kewat¹

2023

Preprint

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—In modern agriculture field, pest and disease identification is a major role of crop cultivation. Image classification by the use of deep convolutional neural networks of training and methodology used the facilitate a quick and easy system implementation. Pests and diseases are a threat to plant production, especially in India, but identification remains to be a challenge in massive scale and automatically. Collecting images from Image Net dataset. The results show that we can effectively detect and recognize the rice diseases and pests including healthy plant class using a deep convolutional neural network, with the best accuracy of 96.50%. The significantly high success rate makes the model a really useful advisory or early warning tool, and an approach that would be further expanded to support an integrated plant disease identification system to work in real cultivation conditions.

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“…The proposed model provided an accuracy of 98.03%. Other researchers, Meeradevi et al applied Deep convolutional neural networks to identify leaf diseases in tomatoes [18]. Militante, S. V et al [19] applied deep learning for identification of sugarcane diseases.…”

Section: Figure1 Groundnut Oil and Vegetable Oil Export From Indiamentioning

confidence: 99%

Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks

Kukadiya,

Meva,

Nidhi Arora

et al. 2023

Int. Res. J. multidiscip. Technovation

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Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model ‘CNN8GN’. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy.

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Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks

Cited by 17 publications

References 8 publications

Techniques of deep learning and image processing in plant leaf disease detection: a review

Techniques of deep learning and image processing in plant leaf disease detection: a review

Plant Disease Classification using Alex Net

Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks

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