Disease Detection of Apple Leaf Using Machine Learning Techniques - Patiballa Subrahmanya Kasyap; Dr. Prasanth Yalla (Utilitas Mathematica, 2025)
Application of farming in sample and identification of diseased leaves is another area where farming can bring a revolution in terms of disease control through the use of machine learning. Building, in essence, works with loopholes with the aid of state-of-the-art computation to learn plant diseases through the depiction of pictures. This process integrates multiple stages, and one of the first is data acquisition: the necessary set of images of leaves of different access was collected, and the method of support SVM’s would be a large space of diseased and non-diseased samples in comparison to the other classifiers. This implies that before getting into the AI model, the data is subjected to several processing steps that yield features suitable for the model. increase the extent of image detail of current objects of interest and acquire new sources for the current corpus. There are basically artificial neural network approaches here, and among these, convolutional neural networks (CNNs) are used most in the current time. This comes from the capacity to regulate and decide on the hierarchy attributes of the distinct pictures and patterns. Model selection reaches even the activity of comparing features of some algorithms to make the correct choice among the available ones, using criteria of efficacy, for instance, including accuracy and precision. In other words, there is a significantly large phase of checking the accuracy of the model in another dataset entirely different from the input data set, whose aim is to determine the generality and overall robustness of the model in any other data set.