Study of digital image processing techniques for leaf disease detection and classification

Dhingra, Gittaly; Kumar, Vinay; Joshi, Hem Dutt

doi:10.1007/s11042-017-5445-8

Cited by 178 publications

(62 citation statements)

References 56 publications

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“…Future instances of genuine AI headway applications are talked about. Gittaly [21], This paper, address an extensive spotlight on confusion assertion and social affair of plant leafs utilizing picture preparing approachs Abu Bakar [22], This paper depicts an arranged framework for exposure of infirmities on leaves called Rice Leaf Blast (RLB) utilizing picture arranging strategy. It joins the picture pre-preparing, picture division and picture assessment where Hue Saturation Value (HSV) hiding space is utilized.…”

Section: Litrecture Reviewmentioning

confidence: 99%

A Novel IoT Enabled System for detection of Infected Leaf for Smart Agricultural

Shukla¹,

Miri²

2021

JUSST

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As we know 2020 is a year which created lots of issues with every type of the business. Even it affects the agricultural eco system. Due to COVID-19 there is very less man power is available and farmers who are doing forming in larger way, they need lots of man power to monitor & maintain their farmers. Due to lack in man power its very difficult for the formers to monitor their farms from remotely, so there is need of system which is able to provide smart agricultural features like temperature & humidity monitoring remotely apart from that farmer have the power to monitor the quality of their leaves. So, in this paper we present an IoT enabled system which is able to track the temperature & Humidity from the remotely apart from that it is also able to capture the image of the leaf and send to server where in server using Computer vision & machine learning system, we will track the leaf quality. In this paper we proposed a hardware unit which is basically using ESP32 Cam and DHT11. Here ESP-32 work as smart CAM which is able to track the image of the leaf and send those real time image to the server. Here we are using think speak to store the accuracy level parameters of each leaf record. Similar we will store temperature & humidity real time data on the think speak server. Think speak server is powered by math work.

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Section: Litrecture Reviewmentioning

confidence: 99%

A Novel IoT Enabled System for detection of Infected Leaf for Smart Agricultural

Shukla¹,

Miri²

2021

JUSST

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“…However, these manual processes take time, are cumbersome and also very subjective [8]. Several methods have been developed in recent years incorporating computer vision to detect and identify agricultural and horticultural crop diseases to address the manual techniques issues [9,10]. Image collection, retrieval of features, filtering of features and classification analysis with parametric or non-parametric statistics are fundamental steps in those processes.…”

Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Yadav¹,

Akanksha²,

Yadav³

2020

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Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

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“…Pengembangan aplikasi analisis digital pada tumbuhan sudah masif dikembangkan. Beberapa aplikasi analisis digital pada tumbuhan, diantaranya (Dhingra et al, 2018;Khirade dan Patil, 2015;Meena Prakash et al, 2018). Khirade dan Patil, (2015) menggunakan metode ANN untuk klasifikasi penyakit pada tanaman.…”

Section: Pembahasanunclassified

Analisis Gambar Digital Untuk Serangan Penyakit Layu Fusarium Di Pisang Menggunakan Imagej

et al. 2020

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Fusarium wilt disease caused by Fusarium oxysporum f. sp. cubense is the most dangerous disease in banana. Recently, development of new banana varieties has been the most effective ways to prevent this disease. To develop the resistant banana, Fusarium severity analysis is the important part in the process of Fusarium disease assessment to quantify the disease severity.. The objective of this study was to develop digital image analysis method for Fusarium severity analysis by using software ImageJ. Pisang Ambon and Pisang Cavendish were used as plant material due to its susceptibility of the disease. Fusarium severity analysis performed as follows (i) Photographing of Fusarium-infected rhizom (ii) Digital image analysis by using ImageJ of the taken image. The analysis result was percentage area of Fusarium-infected rhizom, represented by necrosis and discoloration. The percentage of rhizome infected by Fusarium- of Pisang Ambon#1 was 50.10%, while Pisang Ambon#2 was 22.23%. In addition, the percentage of Pisang Cavendish#1 and Pisang Cavendish#2 was 28.52% and 39.5%, respectively. Digital image analysis of the sample showed consistent result and more objective. Development of the digital image analysis is not only useful for Fusarium severity analysis in Banana, but also for other crops.

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Study of digital image processing techniques for leaf disease detection and classification

Cited by 178 publications

References 56 publications

A Novel IoT Enabled System for detection of Infected Leaf for Smart Agricultural

A Novel IoT Enabled System for detection of Infected Leaf for Smart Agricultural

A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Analisis Gambar Digital Untuk Serangan Penyakit Layu Fusarium Di Pisang Menggunakan Imagej

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