Agriculture, a crucial component of human civilization, provides sustenance and bolsters the economy. Nonetheless, plant foliage and crops are vulnerable to numerous illnesses that might impede their growth. Timely and precise identification and categorization of these disorders are essential to avert additional harm. Conventional techniques employed by agriculturists to forecast and categorize plant ailments can be laborious and susceptible to inaccuracies. Manual prediction can result in considerable crop devastation due to the tardy or erroneous detection of diseases, adversely affecting productivity. The implementation of automated image processing techniques in agriculture can reduce these losses and improve productivity. Numerous techniques have been utilized to identify and categorize plant diseases from photographs of afflicted leaves or crops, resulting in considerable advancements in this domain. Researchers have investigated various methodologies for disease detection and classification, encompassing deep learning (DL) and machine learning (ML) techniques such as k-means clustering (KMC), naive Bayes (NB), feed-forward neural networks (FFNN), support vector machines (SVM), k-nearest neighbor (KNN) classifiers, fuzzy logic (FL), genetic algorithms (GA), artificial neural networks (ANN), and convolutional neural networks (CNN). Convolutional neural networks (CNNs) are especially preferred for image identification and classification because of their capacity to independently learn pertinent image features and comprehend spatial hierarchies. The selection between conventional machine learning and deep learning methodologies is contingent upon the particular problem, data accessibility, and computational resources. Deep learning, particularly via convolutional neural networks, is frequently favored for sophisticated image recognition and classification tasks when ample data and processing resources are available, exhibiting strong performance on particular datasets. This work seeks to furnish future researchers with current data regarding the performance, assessment metrics, and outcomes of previously employed methodologies for the detection and classification of diverse plant leaf or crop diseases.