Pinpointing crop diseases plays a key role in boosting farm output and food quality. However, old-school methods like expert visual checks take considerable time and potentially result in errors. New tech has paved the way to create automated systems that use machine learning (ML) and deep learning (DL) methods. Convolutional Neural Networks (CNNs) have shown they’re good at reaching accuracy levels up to 98% when spotting plant diseases from images. What’s more, new tech like hyperspectral imaging and spectroscopy gives us rich spectral data that can boost disease detection accuracy to about 95%. When we mix these advances with ML algorithms, we see big improvements over the old ways. But we still face some hurdles. We need large, varied datasets, we risk overfitting, and these methods need a lot of computing power. Looking ahead, researchers aim to make models work better across different situations, bring together data from many sources, and create easy-to-use tools for farmers. The steps forward in plant disease classification through this cutting-edge tech are crucial to support sustainable farming and make sure we have enough food.