Tea Leaf Disease Classification Using Deep Learning Techniques

A major contributor to India’s GDP and an integral part of the country’s agricultural sector is the tea production industry. It is now widely acknowledged that India is the leading consumer and number two producer of tea on an international scale. Lots of people rely on this industry as their main source of income, especially in rural areas, where it provides both direct and indirect work opportunities. There has been a dramatic increase in research into image recognition systems due to the meteoric climb of deep learning. This is especially true in fields like automated image classification and plant disease detection. The annual production of tea in India is substantial, which has been good for the economy. However, a major obstacle has emerged for the tea industry: the massive buildup of tea waste caused by tea leaf disease. If we want the sector to continue to thrive, we need to solve this problem. The total yield of the crop could be affected if illnesses slow down the growing rate of tea leaves. Making a CNN model that can detect diseases in tea plants is, hence, the main goal of this research. To do this, a dataset with numerous types of leaf photos will be used. CNN is a great fit for this application since it can rapidly assess many kinds of leaves, such as tea leaves and other plant leaves, which improves the accuracy of diagnostic results. Furthermore, the method under review has analyzed pictures of tea leaves and achieved an accuracy level of 92%. Furthermore, future updates will allow for even greater photo enhancement through the use of augmentation techniques, leading to even better results.