Deep transfer learning driven model for mango leaf disease detection

India exports a big volume of mangoes, the mango fruit holds significant economic and ecological worth in India. Plant diseases are a very typical occurrence that reduces production of mangoes and results in significant losses for farmers. In this regard, healthy output depends on the early detectio...

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Bibliographic Details
Published inInternational journal of system assurance engineering and management Vol. 15; no. 10; pp. 4779 - 4805
Main Authors Singh, Yogendra Pratap, Chaurasia, Brijesh Kumar, Shukla, Man Mohan
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.10.2024
Springer Nature B.V
Subjects
Algorithms
Artificial intelligence
Classification
Datasets
Engineering
Engineering Economics
Logistics
Machine learning
Mangoes
Marketing
Medical imaging
Organization
Original Article
Plant diseases
Plant layout
Quality Control
Reliability
Safety and Risk
India
Machine learning (ML)
Convolutional neural network (CNN)
Deep learning (DL)
Artificial intelligence (AI)
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Summary:India exports a big volume of mangoes, the mango fruit holds significant economic and ecological worth in India. Plant diseases are a very typical occurrence that reduces production of mangoes and results in significant losses for farmers. In this regard, healthy output depends on the early detection of plant diseases. It is quite challenging to identify the disease with the naked eye. Artificial intelligence and machine learning have, therefore, been widely utilized in the agriculture sector for automatic monitoring of food and agricultural goods and have proven to be a scientific and powerful instrument for intensive study over decades. In this paper, we have developed the deep transfer learning driven (DTLD) model to identify mango leaf disease. The suggested model is trained and tested using a variety of complex algorithms, datasets, and validation methods. After performing some preprocessing on the data, we divide it into training and testing datasets. We use the softmax activation function to classify diseases of mango in model’s training and testing. The outcomes demonstrate that the proposed model has obtained 99.76% accuracy to prove the efficacy. Moreover, a dataset containing 4000 images has been used in this endeavor. The proposed DTLD model can successfully classify the image of the mango leaf into different disease.
ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-024-02480-y