Mango Leaf Disease Classification Utilizing InceptionV3 Deep Learning Model

Mango leaf diseases are serious problems to mango farming, affecting both tree health and yield. Hence, the development and use of mango leaf disease detection technology is an important step toward sustainable mango production. This paper predicts mango leaf disease using the InceptionV3 algorithm. The performance is indicated with accuracy and loss for three epochs. First epoch 15 is applied. Training accuracy is 0.9575 and loss is 0.3891; the testing accuracy is 0.9262, and the loss is 0.8125. Modify epoch value to 20 and compute results. This time, the training accuracy and loss will be 0.9619 and 0.3392 respectively. Test accuracy and loss are 0.9388 and 0.7276 respectively. The final value of the epoch is 25 and produces better results. At epoch 25, the training accuracy is 0.9703, the loss is 0.2283; the testing accuracy is 0.9500, and the loss is 0.5020. These findings show that the epoch 25 model performs better.