Development of Ai-Based Crop Quality Grading Systems using Image Recognition

Crops' quality assessment in the current agricultural environment is still a labour intensive process relying much on human judgment. Often traditional crop grading methods are inconsistent of errors and tend to be inefficient and suboptimal grading results which cause costs. This research makes a contribution to the solution of these challenges by proposing a novel and automated crop quality grading system based on the use of advanced image recognition techniques. It also integrate Convolutional Neural Networks (CNN), Transfer Learning, Support Vector Machines (SVM) and Random Forest algorithms to label crop images into pre defined categories. The data given is 10,000 labeled images across five quality grades from various crop types under different lighting conditions for robustness. The system is then evaluated and doesn't perform well, showing that Transfer Learning outperforms other baselines with 95.8% of accuracy, whereas CNN, Random Forest, and SVM get 92.1%, 87.4% and 85.9% respectively. Moreover, the Transfer Learning has the shortest training time, which shows the preference of this approach. Such results indicate that the system can reduce manual labor to a large extent, improve grading precision, and facilitate system integration within the agricultural supply chain. It has also offered a reliable alternative to traditional practices and standardized crop quality grading with a minimum loss of the post harvest and keep the economic outcomes of stakeholders.