KrishiMitr (Farmer’s Friend): Using Machine Learning to Identify Diseases in Plants

Sharma, Parul; Berwal, Yash Paul Singh; Ghai, Wiqas

doi:10.1109/iotais.2018.8600898

Cited by 13 publications

(4 citation statements)

References 5 publications

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“…In order to improve the efficiency of the proposed algorithm a feedback loop was created in post processing step. Sharma et al (2018) proposed a machine learning algorithm for the identification of diseases in plant. Nearly eighty thousand images were collected to form the database.…”

Section: Literature Reviewmentioning

confidence: 99%

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Plant Disease Detection using Deep Learning in Banana and Sunflower

H R,

G S,

et al. 2023

J. Adv. Zool.

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In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

“…But the farmers face a lot of issues due environmental conditions and diseases that occur in the crops. The majority of issues are reduced by offering some technical facilities (Sharma et al, 2018). Finding specialists is not necessary if disease prevention is implemented in a timely manner to increase food yield.…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Detection using Deep Learning in Banana and Sunflower

H R,

G S,

et al. 2023

J. Adv. Zool.

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“…This input is then fed into a machine learning model, specifically a Decision Tree, which processes the data and generates an output displaying the predicted weather for the given city and day.In the plant disease prediction path, the user is asked to input various details such as the estimated count of infected plants, crop type, soil type, pesticide category, number of weeks since the last application of pesticide, and an image of the diseased plant. [12] The input data is then integrated into a dataset, and feature selection, data cleaning, and transformation are performed. The training and testing datasets are used to develop a machine learning model that can predict whether the plant is infected or not and also the name of the disease based on the provided image.…”

Section: Modeling and Analysismentioning

confidence: 99%

Farmwave the Crop Guider

2023

IRJMETS

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Agriculture plays a vital role in our country's social and economic development, but many farmers struggle with maintaining good farming facilities. The government provides various policies to support farmers, and today, farmers are increasingly using the internet and smartphones for smart farming. The proposed system, called Farmwave, aims to make agriculture more connected and smarter by tracking weather conditions and predicting crop disease and proper fertilizer to improve yield quality and reduce costs. This system provides valuable information to farmers and helps manage demand forecasts, bring goods to market, and reduce waste.[2] Overall, agriculture is an important source of livelihood for the majority of our population, contributing to national income and employment. The suggested system aims to monitor weather patterns in a particular area, including temperature, humidity, and environmental shifts. Its purpose is to reduce the time and energy farmers spend by providing them with daily market prices for various crops, fertilizers, and vegetables without requiring them to visit the market. The data, which includes values, climate, and the latest rural techniques, will be transmitted to the farmers via this application. [2] This application is extremely helpful for farmers as it keeps them informed and provides a source of income for a large portion of the population, which contributes to the country's overall revenue and employment opportunities. Also, This application is multilingual which helps to improve customer experience: By providing information in the customer's native language, you can improve their experience on your website, making it easier for them to understand and engage with your content. This can help to build trust and loyalty with your customers.

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“…al. [14]. This deep learning model uses Convolutional Neural Networks (CNN) and run on Tensor flow..…”

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confidence: 99%

Krishimitr: A Cloud Computing and Platform for Disease Detection in Agriculture

Sharma*¹,

berwal²,

Ghai³

2019

IJITEE

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Automating disease detection is a cornerstone in the journey to achieving sustainable agriculture. We describe a framework utilizing Machine Learning, Cloud Computing and Internet-of-Things which brings experts to farmers, allowing for timely detection of diseases. This innovative and comprehensive framework provides agronomists and farmers with a solution for diagnosing plant diseases. By leveraging modern ICT capabilities, this extensible framework is currently trained for over 15 plant types and more than 51 disease types. Our framework employs a hybrid model combining use of both online and offline resources to provide up-to-date information to farmers even in case of patchy connectivity

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KrishiMitr (Farmer’s Friend): Using Machine Learning to Identify Diseases in Plants

Cited by 13 publications

References 5 publications

Plant Disease Detection using Deep Learning in Banana and Sunflower

Plant Disease Detection using Deep Learning in Banana and Sunflower

Farmwave the Crop Guider

Krishimitr: A Cloud Computing and Platform for Disease Detection in Agriculture

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