An Image Processing and Machine Learning Approach for Early Detection of Diseased Leaves

India is largely an agriculture dependent country. It contributes to almost 17% of the GDP. A wide range of crops are grown throughout the year. Extensive cultivation also makes the plants prone to a lot of diseases. There are no efficient methods to detect these diseases from its outset. People in the rural areas where most of the agriculture happens are totally helpless in situations where most of their crops have been affected by disease. Most of the diseases that plague plants leave a characteristic feature on the leaf. By applying image processing techniques like image enhancement and feature extraction one can extract the required information required to analyze the type and severity of the disease. The obtained information when fed to a classifier like support vector machine (SVM), the plant can be classified to be affected by a certain disease. One can also determine the stage of the disease (infant or mid or terminal). Crop diseases impact the livelihood of those involved in agriculture immensely. Consumption of such produce also affects the health of humans and animals. Manually monitoring these diseases requires a lot of time and expertise. Hence, utilizing image processing for the detection of diseases is a better option. It takes into consideration the features which may not be determined visually. Consider the example of tomato crop in India which is prone to a number of diseases caused by pathogens, bacteria, viruses, and phytoplasmas-like organisms. Due to this disease the framers incur a huge loss. To overcome this problem a lot research is being conducted using image processing and neural network model for automatic detection of diseases using drone technology.