Plant Disease Detection Using Machine Learning Tools With an Overview on Dimensionality Reduction

Roy, Saurav; Ray, Ratula; Dash, Satya Ranjan; Giri, Mrunmay Kumar

doi:10.1002/9781119785620.ch5

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…The results of the exploratory study are divided, and cutting-edge work is getting closer. The accuracy of our suggested method in detecting rice plant problems such as leaf scourge, coarse concealed spot, and leaf impact is 89.19%, 82.86%, and 89.19%, respectively [13]. Disturbance and confusion ID is a fundamental control of rice improvement in the current developing sector.…”

Section: Introductionmentioning

confidence: 92%

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Bhosale,

Thorat,

Pancholi

et al. 2023

IJRITCC

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Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important studyarea is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plantillnesses. The process of choosing a classification method is always challenging because the quality of the results can differ depending on the input data. K-Nearest Neighbor Classifier (KNN), Probabilistic NeuralNetwork (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis,Artificial Neural Network (ANN), and Fuzzy Logic are a few examples of diverse classification algorithms.Classifications of plant leaf diseases have many uses in a variety of industries, including agriculture andbiological research. Presymptomatic diagnosis and crop health information can aid in the ability to managepathogens through proper management approaches. Convolutional neural networks (CNNs) are the mostwidely used DL models for computer vision issues since they have proven to be very effective in tasks likepicture categorization, object detection, image segmentation, etc. The experimental findings demonstrate theproposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range ofcategorization accuracy is 76% to 100%, based on.

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Section: Introductionmentioning

confidence: 92%

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Bhosale,

Thorat,

Pancholi

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Our proposed technique has a detection accuracy of 89.20%, 82.87%, and 89.29% for major scourge, fine hidden identify, and leaf effect, accordingly, in rice plants. [17] Agitation and uncertainty in the currently growing industry, ID is a crucial factor in rice development. rice output is threatened by troubling forces and problems, mainly in the nation of India, but ID remains to be a problem in massive growth and frequently.…”

Section: Literature Reviewmentioning

confidence: 99%

Cotton Plants Diseases Detection Using CNN

Nilesh N. Thorat

2023

IJRITCC

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Identifying cotton infections is a major problem that often requires expert assistance in determining and treating the disease. This investigation aims to create a sophisticated learning model that can tell a plant's illness apart from images of its leaves. Convolution Brain Organization is used to do move training to complete deep learning. For the dataset used, this method produced outcomes for a given state of quality. The main goal is to offer this approach to as many individuals as is realistically expected while reducing the cost of professional aid in identifying cotton plant diseases. The ability to recognize and understand items from photographs has been made possible by rapid advancements in deep learning (DL) techniques.

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“…Li et al (2018) found another way with three observed features represented as 3-D vector features: color, texture, and vein feature. Recently, machine learning techniques provide efficient results in terms of dimensionality reduction (Roy et al, 2021). Guma et al (2018) has been discussed farmers' constraints for better food production.…”

Section: Importance Of Growth Analysis For Early Disease Detectionmentioning

confidence: 99%

Statistical Growth Analysis of Rice Plants in Chhattisgarh Region Using Automated Pixel-Based Mapping Technique

Patel

Sharaff

Verulkar

2022

International Journal of System Dynamics Applications

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The statistical growth analysis of field crop has become a great challenge in agriculture. Analyzing the growth of crop through automation provides extensive significance to the farmers for getting information about the problem arising in plants due to irregular growth monitoring. The idea behind this work is the importance of mapping with pixel-based clustering technique for growth analysis in terms of height calculation of rice crop (rice variety is MTU-1010). Height measurement plays a vital role in regular assessment for a healthy crop, and the approach proposed in this work achieves 97.58% accuracy of 14 sampled datasets taken from Indira Gandhi Agriculture University of Raipur, Chhattisgarh; a real-time dataset has been prepared. Proposed work is used for analyzing vertical as well as horizontal scaling technique. Vertical mapping provides the height of a single plant whereas horizontal mapping using k-means clustering provides an average height of the whole field. This work uses machine learning, and image processing techniques are used for this work.

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Plant Disease Detection Using Machine Learning Tools With an Overview on Dimensionality Reduction

Cited by 9 publications

References 46 publications

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Cotton Plants Diseases Detection Using CNN

Statistical Growth Analysis of Rice Plants in Chhattisgarh Region Using Automated Pixel-Based Mapping Technique

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