인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
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.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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