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Identification, Classification, and Grading of Crops Grain Using Computer Intelligence Techniques: A Review
Abstract
India is the second-largest food producer globally, trailing only in China. However, significant agricultural losses occur because of the lack of skilled laborers. Harvested commodities often go into waste. Additionally, the imprecise nature of crop identification, classification, and quality inspection, which is influenced by human subjectivity, poses challenges. To address these issues and reduce labor costs, the agricultural sector must embrace automation. Developing an automated system capable of distinguishing between various crops based on their texture, shape, and color is feasible by employing appropriate image-processing techniques and machine-learning methods. This study focuses on advancing the state-of-the-art research in this field. It briefly explores recent research publications' methodologies, comparing them using diverse techniques, such as k-nearest neighbors (KNN), artificial neural networks (ANN), random forest (RF), naive bayes (NB), backpropagation neural networks (BPNN), support vector machines (SVM), and convolutional neural networks (CNN).
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