Approach for Chilli Disease Diagnose and prediction through Machine Learning Algorithms

To detect diseases in chili plants using deep learning, we first need a large set of images that include both healthy plants and those affected by various diseases, such as Anthracnose or Bacterial Wilt. These images must be carefully labelled so the model can learn to differentiate betweenhealthy anddiseasedplants. Oncewehavetheimages,weneedto preparethemfor training the deep learning model. This preparation includes resizing the images to a standard size, normalizingthepixelvaluesforconsistency,andaugmentingthedatabyapplying transformationslikerotatingortippingtheimages.Thesestepshelpthemodelrecognizeplants in various conditions and increase the amount of data available for training. The core of the system reliesonConvolutionalNeuralNetworks(CNNs),whicharespecializedforanalyzingimages. To improve performance, we can use a technique called transfer learning, where we take a modelthathas already been trainedon large datasets (likethose used for general image recognition) and ne-tune it with our specific chili plant data. This approach allows the model to learn to detect plant diseases more effectively, even if the dataset is small. After training, the model can automatically classify images of chili plants.