Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

Rishiikeshwer, B.S.; Shriram, T. Aswin; Raju, Jayabharathy; Hari, Misha; Santhi, B.; Brindha, G.

doi:10.3844/jcssp.2020.158.166

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Training and test sets are 99.38% and 78.33%. [34] The bilateral image filtering and CGAN. Accuracy of 96.4%.…”

Section: 08% Accuracy [30]mentioning

confidence: 99%

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Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.

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“…Training and test sets are 99.38% and 78.33%. [34] The bilateral image filtering and CGAN. Accuracy of 96.4%.…”

Section: 08% Accuracy [30]mentioning

confidence: 99%

“…[33] VGG16 model Accuracy 99.8%. [34] CNN MODEL By using Adam optimizer and Tanh activation function, 98.08% accuracy. [36] Transfer learning using eleven CNN models.…”

Section: Modelmentioning

confidence: 99%

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Dinesh,

Lakshmanan

2023

IJRITCC

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“…A real-time autonomous plant disease detection system applying different deep Learning architectures was proposed by Rishikeshwar et al [5]. After performing hyper parameter optimization, the model generates accuracy levels of up to 95% with 400 real leaf images and up to 98% with 3600 enhanced datasets.…”

Section: Introductionmentioning

confidence: 99%

Image based Chili Crop Disease Prediction Using Deep Transfer Learning

Pallepati Vasavi

2023

IJRITCC

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Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challenge, with deep learning becoming as the preferred approach due to its outstanding performance.This research describes a method for detecting chili leaf diseases using a deep convolutional neural network. We compared performances of four architectures: MobileNet, Inception-ResnetV2, EfficientNetB0, and DenseNet. The proposed approach evaluated the findings using measures such as accuracy, loss and time. Our model compares favorably to EfficientNetB0 with an accuracy of 0.995, a loss of 0.023, and time is 5 minutes 45 seconds. EfficientNetB0, a compact deep learning architecture has fine tuned to classify two forms of chili leaf diseases. The method was tested on 2475 photos from the Plant Village dataset.

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Machine Learning and Deep Learning for Medical Analysis—A Case Study on Heart Disease Data

Swetha

Santhi

Brindha

2022

Fundamentals and Methods of Machine and Deep Learning

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Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks

Cited by 8 publications

References 8 publications

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

Image based Chili Crop Disease Prediction Using Deep Transfer Learning

Machine Learning and Deep Learning for Medical Analysis—A Case Study on Heart Disease Data

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