Banana Leaves Diseases and Techniques: A Survey

Patel, Ankita; Agravat, Shardul J.

doi:10.1007/978-981-15-4474-3_24

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Deep learning algorithms outperform machine learning algorithms [15], albeit suffering from the same overfitting problem. For CNN classification, the images are shrunk to 150x150 pixels, whereas those used to train on the Alexnet [16] dataset are expanded to 250x250. By adjusting the size of the dataset, we can speed up the training of the algorithms.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, if they are caught in time, they can be dealt with, and the plant salvaged. Patel et al (2021) note that a method to automatically identify and recognize banana diseases is crucial considering the global shortage of resources and abilities in banana pathology [16]. Bananas of different varieties may be rendered unusable if their vasculature is severely damaged by environmental stresses (such as soil type, temperature, crop intensification, drainage, and so on).…”

Section: Related Workmentioning

confidence: 99%

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Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Aliff,

Luqman,

Yusof

et al. 2024

ITM Web Conf.

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The primary agricultural pursuit in Malaysia centres around banana cultivation; however, this vital crop faces the daunting challenge of multiple diseases that hinder its growth. The adverse consequences of these diseases extend beyond the farms to impact the nation’s economy. To empower farmers with the tools to promptly identify and categorize these diseases, image processing techniques offer a valuable solution. This research leverages deep learning Convolutional Neural Networks (CNN) implemented through MATLAB in conjunction with a DJI drone. By harnessing this technology, the system can automatically detect and classify major banana diseases. The study meticulously fine-tuned several hyperparameters to achieve impressive training and testing accuracy levels. The results revealed that the model attained its highest training accuracy of 81.27% at epoch 8 and its lowest accuracy of 78.40% at epoch 4, demonstrating its potential to aid in early disease detection and classification in banana crops.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Aliff,

Luqman,

Yusof

et al. 2024

ITM Web Conf.

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“…Some classification techniques such as SVM, ANN, and KNN are studied and compared based on the accuracy and performance. Patel A. et al, (2021), made a brief report on the image processing tools and machine learning algorithms that are used to identify the variety diseases affecting the banana plant. The other methods and technologies used in the detection and classification processes are also analyzed and discussed.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

John

Arul

2021

INMATEH

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As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

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Banana Leaf Diseases and Machine Learning Algorithms Applied to Detect Diseases: A Study

Gupta

Jain

2022

Emerging Technologies in Data Mining and Information Security

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Banana Leaves Diseases and Techniques: A Survey

Cited by 5 publications

References 5 publications

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

Banana Leaf Diseases and Machine Learning Algorithms Applied to Detect Diseases: A Study

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