The early detection of leaf diseases is vital for maintaining plant health and boosting agricultural productivity, especially in a country like India, where agriculture plays a pivotal role in the economy. Among the myriad of plants grown in the country, the tulsi plant (Ocimum sanctum), commonly referred to as holy basil, holds a unique position due to its medicinal, cultural, and economic significance. Tulsi leaves are extensively used in Ayurvedic medicine, culinary preparations, and religious rituals, making the plant a valuable asset. However, like many other crops, tulsi plants are prone to various diseases, which can severely impact both their yield and quality. These diseases, often caused by fungal infections, pests, or environmental stress, not only reduce the economic returns for farmers but can also compromise the medicinal properties of the leaves.

Despite their importance, traditional methods for detecting diseases in tulsi plants largely rely on manual inspection, which is time-consuming, labor-intensive, and often inaccurate. These manual processes require trained experts to identify disease symptoms based on visual cues such as discoloration, spots, or deformities. However, such methods are not scalable, especially for large-scale agricultural operations, and they are susceptible to human error. Furthermore, delayed detection of diseases can lead to their rapid spread, exacerbating crop losses. Thus, there is an urgent need for automated, efficient, and scalable solutions that can detect diseases in tulsi plants at an early stage, enabling farmers to take timely preventive and corrective measures.

Hardware Requirement:
1.Pc

Software Requirement:
1.Python idle 3.8

Algorithm:
1.CNN2D
2.VGG16
3.Resnet50

Library:
1.Keras
2.Tensorflow
3.Scikit-learn
4.Numpy
5.Pillow
6.Opencv-python

*order from online