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인공지능 기반 밭작물 생산량 모니터링 기술 동향

Trend of Artificial Intelligence Techniques for Monitoring Field-crop Productivity

조수빈|김성윤|김건우|조은아|최지원|권경도|조병관
농업생명과학연구원 (Institute of Agriculture & Life Science, Gyeongsang National University)|2023|농업생명과학연구 (Journal of Agriculture & Life Science)|제57권 제5호|pp. 1-11

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인공지능Machine learning머신러닝Artificial intelligence딥러닝Deep learning생산량밭작물Crop yieldField crops

초록

식량 작물의 확보 및 생산량 예측은 국가 발전에 있어 필수적이며, 국가 경제뿐만 아니라 전 세계 식량 안보에 기여 한다. 최근 환경오염으로인한 이상기후는 식량 작물 생산량에 직ㆍ간접적으로 부정적 영향을 끼치고 있어, 작물 수확량 예측 불확실성이 높아지고 있다. 특히, 노지 작물의경우 생산량 감소와 품질 저하 문제가 화두 되고 있다. 이러한 문제는 농가들뿐만 아니라 소비자들에게도 큰 피해를 안겨주고 있다. 이러한 생산량예측 이슈를 해결하기 위해 최근에는 인공지능 기술이 농업 분야에도 활발히 적용되고 있다. 작물 수확량의 정확한 예측을 위한 머신러닝 기반연구가 집중적으로 수행되고 있다. 따라서, 본 연구에서는 이와 같은 인공지능 기반의 노지 작물 수확량 예측 기술(머신러닝, 딥러닝, 하이브리드모델 등) 현황 및 작물 수확량에 가장 영향을 많이 끼치는 모델 파라미터 등을 조사하였다.

Abstract

Securing food crops and predicting its productivity are essential for national development and contribute not only to the nationaleconomy, but also to global food security. Recent abnormal weather patterns caused by environmental pollution directly and indirectlyaffect food crop production, leading to increased uncertainty in crop yield predictions. In particular, because of the abnormal weatherconditions, the low productivity and quality of field crops has attracted substantial attentions in agricultural industry. This issues notonly affect farmers, but also cause significant damage to consumers. To solve the prediction issues, various artificial intelligence technologieshave recently been actively applied in the field of agriculture. To accomplish this, machine learning-based researches on accurately predictingcrop yields are being intensively conducted. Therefore, in this study, we investigated the current status of AI-based field crop yieldprediction technologies (such as machine learning, deep learning, and hybrid models) and major parameters that have the greatest impacton crop yield.

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