Analysis of Classification Algorithms for Plant Leaf Disease Detection

Reddy, J. Nithiswara; Vinod, Karthik; S, Remya Ajai A

doi:10.1109/icecct.2019.8869090

Cited by 45 publications

(4 citation statements)

References 17 publications

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“…J.Nithiswara Reddy [3]With minimal computing effort, the proposed method can greatly support a precise diagnosis of leaf diseases. They developed framework software in Matlab to identify plant leaf diseases by using methods for processing images.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognize and classify illnesses on tomato leaves using EfficientNet's transfer learning approach with different size dataset

Buchke,

Mayuri

2024

SIViP

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This study focuses on the remarkable progress made by the agricultural sector in utilizing image processing techniques for early detection and classi cation of leaf plant diseases. Timely identi cation of diseases is crucial, but it often poses a challenge for the human eye to discern subtle differences. To address this issue, the researchers propose a novel approach that employs E cientNet, a deep learning model, to accurately recognize various diseases affecting tomato plant leaves. Transfer learning is applied to three different datasets comprising 3000, 8000, and 10,000 images of diseased tomato leaves. The experimental results demonstrate impressive overall accuracies of 97.3%, 99.2%, and 99.5% when using 3000, 8000, and 10,000 images, respectively, for the detection of common tomato plant diseases. This research underscores the effectiveness of image processing and deep learning techniques in achieving precise and e cient detection of tomato leaf diseases. It signi cantly contributes to the advancement of precision agriculture and enhanced crop management practices.

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Section: Literature Reviewmentioning

confidence: 99%

Recognize and classify illnesses on tomato leaves using EfficientNet's transfer learning approach with different size dataset

Buchke,

Mayuri

2024

SIViP

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“…Using the deep learning, strawberry fruits and leaves, diseases are diagnosed [18]. A convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm-based method for tomato leaf disease detection and classification [19,20].…”

Section: Related Work In the Literaturementioning

confidence: 99%

Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks

Bharathi¹,

Sonai²

2022

Artificial Intelligence

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Nowadays, agriculture plays a major role in the progress of our nation’s economy. However, the advent of various crop-related infections has a negative impact on agriculture productivity. Crop leaf disease identification plays a critical role in addressing this issue and educating farmers on how to prevent the spread of diseases in crops. Researchers have already used methodologies such as decision trees, random forests, deep neural networks, and support vector machines. In this chapter, we proposed a hybrid method using a combination of convolutional neural networks and an autoencoder for detecting crop leaf diseases. With the help of convolutional encoder networks, this chapter presents a unique methodology for detecting crop leaf infections. Using PlantVillage dataset, the model is trained to recognize crop infections based on leaf images and achieves an accuracy of 99.82%. When compared with existing work, this chapter achieves better results with a suitable selection of hyper tuning parameters of convolution neural networks.

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“…Using GLCM (Gray Level Co-occurrence Matrix), the texture, shape, and pixel value are extracted. One of the extracted features is taken into account and correlated with the value of the database and then made on the basis of this judgment [12]. Any of the principal characteristics are:…”

Section: Feature Extractionmentioning

confidence: 99%

Smart Harvest Diseases Identification using Image Processing

Roshan¹,

Dutta

Roy³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Manual identification of leaf or fruit diseases is nearly impossible when it comes to large scale production, and it is a very time-consuming task that also requires a large amount of manpower. Hence, the existing technology is used where the disease identification is done within minutes. When a code is developed and deployed in any of the surveillance drones, a large-scale area can be covered in minutes and with less manpower. Here, in this proposal, there are two methods proposed. One is using the keywords and the other method is where a classifier is trained with the database available and the classifier with the highest accuracy is tested against the test set and a result is obtained. When there is a huge area of land, manually identifying the leaf diseases may require a lot of manpower and also be time-consuming. Hence, deploying this code in any sprayer drone will help in identifying the diseases within minutes. Along with disease identification, the particular longitude and latitude of the disease affected leaves’ location will be saved due to the fact that when the land is huge, the farmer need not go in search of the location; instead, a drone will fly to the exact location and the respective fertiliser will be sprayed. Before identification of leaf disease, a photo of the diseased leaves will be captured, due to which, by looking at the picture, the farmer can decide whether medication is necessary or not.

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Analysis of Classification Algorithms for Plant Leaf Disease Detection

Cited by 45 publications

References 17 publications

Recognize and classify illnesses on tomato leaves using EfficientNet's transfer learning approach with different size dataset

Recognize and classify illnesses on tomato leaves using EfficientNet's transfer learning approach with different size dataset

Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks

Smart Harvest Diseases Identification using Image Processing

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