• Shreyas Varade
• Trupti Mate
• Ankush Kharche
• Ishwari Gadbail

To achieve a good yield for farmers, it is crucial to focus on seed quality. Detecting unhealthy and damaged seeds is crucial in seed quality analysis. This project proposes an automated system introduced to do so that works on CNN. CNN is a type of artificial intelligence that can be trained to recognize patterns in images. The objective of the project is to develop a natural machine learning-based system that can be used to automatically detect and monitor defects in the seeds based on the shape, size, texture, and colour of the seeds. In this project, the support vector machine algorithm will be used to compare which algorithm is best for the model. By training the algorithm on a dataset of labelled seeds, the algorithm will be able to learn the characteristics of high-quality seeds and can then be used to classify new seeds. This research aims to provide insights and methods for farmers to make informed decisions about seed quality, ultimately leading to better crop yields.

CNN (Convolutional Neural Network), SVM (Support Vector Machine), Seeds, Quality, Parameters.

"Seed Quality Monitoring Using CNN and SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.447-452, May-2023, Available :http://www.jetir.org/papers/JETIRFX06077.pdf

"Seed Quality Monitoring Using CNN and SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp447-452, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06077.pdf