A 3-Stage Method for Disease Detection of Cotton Plant Leaf using Deep Learning CNN Algorithm

Agriculture is one of the significant occupation in various countries including India. As major part of the Indian financial system is reliant on agriculture production, the intense consideration to the concern of food production is essential. The nomenclature and recognition of crop infection got much significance in technical as well as economic in the Agricultural Industry. While keeping track of diseases in plants with the support of experts can be very expensive in agriculture region. There is a necessity for a method or system which can automatically identify the diseases as it can bring revolution in monitoring enormous fields of crop and then plant leaflet can be taken ca The detection of cotton leaf disease is a very important factor to prevent serious outbreak.re imme4diately after recognition of disease. The aim of this paper is to provide guidelines for the development of application which recognizes cotton plant leaf diseases. For availing this user need to upload the image of the cotton leaf and then with the help of image processing one can get a digitized colour image of a diseased leaf which can be further processed by applying CNN algorithm to predict the actual root cause for the cotton leaf disease.

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House Plant Leaf Disease Detection and Classification Using Machine Learning

10.4018/978-1-7998-8161-2.ch002 ◽

2022 ◽

pp. 17-26

Author(s):

Bhimavarapu Usharani

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Bacterial Infections ◽

Clustering Algorithm ◽

Disease Detection ◽

Diseased Leaf ◽

Lower Body ◽

Linear Classification ◽

Plant Leaf ◽

Leaf Disease

Hibiscus is a fantastic herb, and in Ayurveda, it is one of the most renowned herbs that have extraordinary healing properties. Hibiscus is rich in vitamin C, flavonoids, amino acids, mucilage fiber, moisture content, and antioxidants. Hibiscus can help with weight loss, cancer treatment, bacterial infections, fever, high blood pressure, lower body temperature, treat heart and nerve diseases. Automatic leaf disease detection is an essential task. Image processing is one of the popular techniques for the plant leaf disease detection and categorization. In this chapter, the diseased leaf is identified by concurrent k-means clustering algorithm and then features are extracted. Finally, reweighted KNN linear classification algorithms have been used to detect the diseased leaves categories.

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Examination and Identification of Plant Leaf Disease Using Hybrid Algorithm

International Journal of Pharmaceutical Research ◽

10.31838/ijpr/2020.sp3.059 ◽

2020 ◽

Vol 12 (sp3) ◽

Keyword(s):

Hybrid Algorithm ◽

Plant Leaf ◽

Leaf Disease

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A Novel Method of Plant Leaf Disease Detection Based on Deep Learning and Convolutional Neural Network

2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) ◽

10.1109/icsp51882.2021.9408806 ◽

2021 ◽

Author(s):

Xulang Guan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Disease Detection ◽

Plant Leaf ◽

Leaf Disease ◽

Novel Method

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A Review of Machine Learning Approaches in Plant Leaf Disease Detection and Classification

2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) ◽

10.1109/icicv50876.2021.9388488 ◽

2021 ◽

Author(s):

Majji V Applalanaidu ◽

G. Kumaravelan

Keyword(s):

Machine Learning ◽

Disease Detection ◽

Learning Approaches ◽

Plant Leaf ◽

Leaf Disease

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Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

Symmetry ◽

10.3390/sym13030511 ◽

2021 ◽

Vol 13 (3) ◽

pp. 511

Author(s):

Syed Mohammad Minhaz Hossain ◽

Kaushik Deb ◽

Pranab Kumar Dhar ◽

Takeshi Koshiba

Keyword(s):

State Of The Art ◽

Computational Cost ◽

Region Of Interest ◽

Number Of Clusters ◽

Plant Leaf ◽

Leaf Disease ◽

Automatic Initialization ◽

Adequate Accuracy ◽

Model Size ◽

High Computational Cost

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

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