The economy heavily depends on agriculture; therefore, automating agricultural activities is becoming more and more important worldwide. Nonetheless, a lot of significant farming areas are referred to as underdeveloped for a variety of reasons, such as not using or lacking access to contemporary technologies. Furthermore, the production of agriculture greatly depends on the quality inspection of seeds. High physiological quality seeds exhibit consistent seedling establishment and a greater germination capacity. Conventional techniques for detection, like electrophoretic analysis, hollow determination, chemical treatment, and hyperosmotic germination (where hyperosmotic solution is used to artificially induce seed germination) are laborious and ineffective. Thus, nondestructive technique using artificial intelligence (AI) holds promise for quick seed quality assessment. Methods that fuse deep learning or classical machine learning with image processing are frequently employed as a substitute for costly, time-consuming, subjective, or damaging measures. To obtain information about the internal or external structures of seeds, a variety of AI tools can be helpful, including hyperspectral imaging, 3D laser scanning, etc. Image features can offer useful information on seed properties that might not be obvious to the unaided eye. Various machine learning methods can be employed to create models based on specific picture features that can be used to differentiate between distinct seed samples and forecast attributes related to seed quality. These models can be useful for identifying different seed species and varieties, breeding initiatives, evaluating the impact of cultivation conditions on seed quality, grading and sorting seeds, evaluating the impact of processing and storage on seed quality, and identifying abnormalities, defects, or diseases in seeds. Fuzzy logic, evolutionary algorithms, and artificial neural networks are examples of strong computer-based technologies that can be used to predict ideal conditions and clarify essential aspects for high-quality seed. Thus, there are several benefits of using AI technologies for seed quality checking, especially for cereals, pulses, and oilseed crops along with their state of seed application.