Deep Learning Precision Farming: Grapes and Mango Leaf Disease Detection by Transfer Learning

Rao, Uchilla S. Vishal; Swathi, R.; Sanjana, V; Arpitha, L; Chandrasekhar, K; Chinmayi,; Naik, Pramod Kumar

doi:10.1016/j.gltp.2021.08.002

Cited by 83 publications

(22 citation statements)

References 10 publications

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“…In terms of accuracy, the B4 models and the EfficientNet B5 outperformed other models. AlexNet was utilized by Rao et al [ 23 ] for feature extraction and automated classification; this method used a pre-trained CNN model. When the system was developed using MATLAB, its detection accuracy rates for mango and grape leaves were 89 and 99%, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Ashwini

Sellam

2023

Neural Comput & Applic

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Corn disease prediction is an essential part of agricultural productivity. This paper presents a novel 3D-dense convolutional neural network (3D-DCNN) optimized using the Ebola optimization search (EOS) algorithm to predict corn disease targeting the increased prediction accuracy than the conventional AI methods. Since the dataset samples are generally insufficient, the paper uses some preliminary pre-processing approaches to increase the sample set and improve the samples for corn disease. The Ebola optimization search (EOS) technique is used to reduce the classification errors of the 3D-CNN approach. As an outcome, the corn disease is predicted and classified accurately and more effectually. The accuracy of the proposed 3D-DCNN-EOS model is improved, and some necessary baseline tests are performed to project the efficacy of the anticipated model. The simulation is performed in the MATLAB 2020a environment, and the outcomes specify the significance of the proposed model over other approaches. The feature representation of the input data is learned effectually to trigger the model's performance. When the proposed method is compared to other existing techniques, it outperforms them in terms of precision, the area under receiver operating characteristics (AUC), f1 score, Kappa statistic error (KSE), accuracy, root mean square error value (RMSE), and recall.

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Section: Literature Reviewmentioning

confidence: 99%

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Ashwini

Sellam

2023

Neural Comput & Applic

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“…Sharada P et al [3] used AlexNet to identify the disease of 14 different plant and get a result of 99.34%. This model is also used by U Sanath Rao, et al [5] on mango leaves , grape leaves and the author get an accuracy of 99% and 89%. On Plant Village dataset, Murk Chohan et al [4] used CNN with pooling layers and convolution and achieved an accuracy of 98.3%.…”

Section: Literature Reviewmentioning

confidence: 98%

Smart Detection and Diagnosis of Plant Disease Using Deep and Machine Learning Methods

2023

IRJMETS

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In a time of lower crop yields and rampant plant diseases, techniques like deep learning and machine learning aided by developed computer vision help increase the crop yield and classify, prevent as well as cure plant-related diseases. In the literature review section, we have quoted many instances of the CNN model and methods such as transfer learning, apple forest and others for the detection and classification of diseases on different crops with their spectacular results in numeric form. We have relied on quantitative secondary data to bring new datasets with unique results. Besides, we jotted down a brief introduction to some Machine and deep learning models. In the next and most important section, we evaluated the accuracy of these pre-trained learning models and classical machine learning algorithms by adding thousands of images of plants infected with different plant diseases, dividing these images into training and Test sets by 70/30 proportion, and by using metrics of Performance, precision, accuracy, and recall. Before conclusion, the accuracy and loss were determined by applying every image from the test dataset to each iteration.

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“…In the study of [24], authors developed a CNN based on AlexNet architecture and using TL to detect and classify Mango and Grapes leaf diseases. They achieved an accuracy rate of 89% and 99% respectively for mango and grapes leaves.…”

Section: Automatic Diagnosis Based On Deep Learningmentioning

confidence: 99%

“…This makes the feature extraction phase difficult. Compared to laboratory conditions disease identification in real-time condition is a very challenging task [7] [14] [24].…”

Section: Potential Challenges In Automatic Diagnosis Of Mango Diseasesmentioning

confidence: 99%

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Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

Faye¹,

Diop²,

Dione³

2022

JCC

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The mango crop suffers from several diseases that reduce both the production and the quality of the mangoes. This also reduces its price on the international market. Diagnosis of these diseases remains difficult in many countries due to poverty and lack of infrastructure. Plant pathologists use several techniques to identify these diseases. But these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. In the last decade, researchers have proposed several solutions in the field of automatic diagnosis of mango diseases. Such solutions are based on Machine Learning (ML) and Deep Learning (DL) algorithms. In this paper, we divided these solutions into two groups: solutions based on classical ML algorithms, and those based on DL. In recent years, DL, especially Convolutional Neural Network (CNN) has become the most widely used method by researchers because of its impressive performance. The critical analysis of the proposed solutions has allowed us to identify their limits and potential challenges in mango disease automatic diagnosis.

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Deep Learning Precision Farming: Grapes and Mango Leaf Disease Detection by Transfer Learning

Cited by 83 publications

References 10 publications

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Smart Detection and Diagnosis of Plant Disease Using Deep and Machine Learning Methods

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

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