Disease Recognition in Sugarcane Crop Using Deep Learning

Malik, Hashmat Shadab; Dwivedi, Mahavir; Omkar, S. N.; Javed, Tahir; Bakey, Abdul; Pala, Mohammad Raqib; Chakravarthy, Akshay

doi:10.1007/978-981-15-3514-7_17

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…Furthermore, object detection algorithms such as YOLO and Faster R-CNN were utilized to accurately localize infected regions, achieving a top mean average precision score of 58.13% on the test set. These findings underscore the effectiveness of CNN-based approaches in automated disease recognition systems even under diverse conditions encountered in realistic scenarios [5].…”

Section: Related Workmentioning

confidence: 64%

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Classification of Sugarcane Leaf Disease with AlexNet Model

KURSUN,

YASIN,

KOKLU

2024

icisna

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This study evaluates the influence of activation functions on the performance of the AlexNet deep learning model in classifying sugarcane diseases. Two popular activation functions, ReLU and LeakyReLU, were compared in terms of classification accuracy and computational efficiency. The ReLU function, known for its simplicity and speed, achieved an accuracy of 87.90% with a total training and testing time of 47 minutes. In contrast, LeakyReLU, which allows a small gradient when the input is negative and hence provides continuity in the learning process, obtained a higher accuracy of 90.67%, albeit at a higher computational cost, taking 54 minutes for the training and testing phase. These results highlight the trade-off between model accuracy and computational time in the deployment of deep learning models for agricultural applications. The study suggests that while LeakyReLU can lead to more accurate models, ReLU remains a competitive choice when efficiency is paramount. Future research should focus on optimizing the balance between accuracy and speed, potentially through the tuning of LeakyReLU parameters or the development of hybrid models.

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Section: Related Workmentioning

confidence: 64%

“…Detection and classification utilize Support Vector Machine (SVM), achieving an average accuracy of 95%. Additionally, the system suggests control measures upon disease identification [6].…”

Section: Related Workmentioning

confidence: 99%

Classification of Sugarcane Leaf Disease with AlexNet Model

KURSUN,

YASIN,

KOKLU

2024

icisna

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“…Malik et al [29] explained the sugarcane crop disease detection using deep learning models such as VGG-19, RseNet-34, and Resnet-50. Five sugarcane diseases have been taken into consideration, and images of the situations were taken using cameras at various resolutions and illumination levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf disease severity classification with explainable artificial intelligence using transformer networks

2023

IJATEE

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The agricultural sector is considered India's most crucial sector. In India, farming is the most common occupation and a significant source of revenue [1]. India is second in the world in terms of population, with 70% of its people living in villages and relying primarily on agriculture for their subsistence. Farmers cultivate a wide variety of crops based on numerous factors such as crop conditions, the environment, soil conditions, local farming practices, new variants of pathogens and various illnesses, etc. *Author for correspondence Presently farmers are incurring losses due to changes in climatic conditions, plant disease is one of the main challenges to food security since it significantly lowers crop quality and output [2].Therefore, it has been difficult to diagnose diseases correctly and discover them early. A variety of methods, including soil management, crop rotation, irrigation, genetic modification, harvesting methods, precision farming, weed control, and pest and disease control, are used to boost the yield. So, it is crucial to identify pesticides and diseases early if you want to boost a crop's production. The traditional method of identifying plant diseases is by visual inspection [3]. This can result in incorrect disease identification, and

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“…In another research work [8] also SVM algorithm is used to detect disease in sugarcane. In research works [9][10] deep learning methods are used for detecting diseases.…”

Section: Related Workmentioning

confidence: 99%

Disease detection in plant leaves using segmentation and autoencoder techniques

Durairaj¹,

Surianarayanan²

2020

Malaya J. Mat

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Efficient detection of plant diseases in agriculture is an important topic of research as the diseases in plants will directly influence crop production, quality of crops and agricultural economy. Though image processing techniques and classification algorithms are useful in automatic detection of diseases, still detection with adequate or enhanced accuracy is an open issue. Recently in contrast to traditional machine learning algorithms deep neural networks are being used for more accurate prediction tasks owing to their capability in solving complex problems. In this work an approach based on autoencoder technique is proposed for detection of diseases in plant leaves with an intention of improving the accuracy of detection. After preprocessing the images of plant leaves, the images are segmented into the normal and affected portions of the images using FCM algorithm. Features are extracted from the segmented clusters by constructing Discrete wavelet transform (DWT) based Gray-level Co-occurrence Matrix (GLCM). The extracted features have been detected for the presence of diseases using deep autoencoder technique. With typical datasets, the accuracy obtained using autoencoder technique is found to be higher than that obtained using conventional approach.

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Disease Recognition in Sugarcane Crop Using Deep Learning

Cited by 14 publications

References 18 publications

Classification of Sugarcane Leaf Disease with AlexNet Model

Classification of Sugarcane Leaf Disease with AlexNet Model

Leaf disease severity classification with explainable artificial intelligence using transformer networks

Disease detection in plant leaves using segmentation and autoencoder techniques

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