Image Processing and Machine Learning for Plant Disease Detection

Agriculture ensures that everyone has enough to eat even if the global population suddenly doubles. Prediction of plant diseases at an early stage is suggested in agriculture since it is crucial to provide food for the general population. Unfortunately, early disease forecasting is not possible for crops. The purpose of this study is to educate agriculturalists on recent advances in the fight against plant leaf diseases. To identify leaf illnesses in tomato plants, an accurate methodology was developed utilizing machine learning and image processing approaches. The authors of this paper propose a method for detecting illness in plants and crops that employs digital image processing and machine learning. The system can distinguish between healthy and unhealthy plant photos by using a Machine Learning model known as Support Vector Machine (SVM). “The Informational properties of leaf samples are extracted using different descriptors, including Discrete Wavelet Transform, Principal Component Analysis, and Grey Level Co-occurrence Matrix. With an F1 score of 99%, 99% accuracy, 98% precision, and 99% recall, the suggested technique integrates Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), Grey-Level Co-occurrence Matrix (GLCM), and Convolutional Neural Networks (CNN)” for the greatest performance”.