An Automatic Approach for Identification of Anthracnose Disease from Mango Leaves Using Soft Computing Techniques

Resumen

Nowadays the concept of healthy environment has gained a lot of importance. The distractions and abnormalities that affect the environment will also affect the entire living organisms. Plants play a significant role in human life and the environment. Healthy plants produce a healthy environment. Also, the plants contribute a lot of benefits to living organisms. Leaves are the major source of food, medicine, water regulations, air purification’s and photosynthesis. It is observed that large number of plant diseases that affects the productivity and growth of a plant. It is not possible to diagnosis plant diseases by individual effort. In recent days, many researchers have developed and discussed various automatic techniques that are available for detecting the plant diseases. Among the available techniques, Soft computing techniques seem to be more relevant compared to other approaches because of its adaptive nature. In this paper, a method called Bacterial Foraging Optimization based Learning Vector Quantization for segmentation and identification of Anthracnose disease on mango leaves has been discussed. This approach also uses Scale Invariant Feature Transform algorithm for feature extraction.