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 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.