• 한국농림기상학회지
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• 제21권2호
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• pp.75-84
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• 2019

이 연구의 목적은 random forest 를 활용하여 기상요소만을 이용하여 우리나라 전체의 벼 평균수량을 예측하는데 있다. Random forest 는 예측에 사용되는 각 predictor variable 을 분리할 수 있는데 이를 통해 분리된 시계열 상의 추세가 비정상적인 증가형태를 보였다. 이는 결국 예측능력의 저하로 이어지기 때문에 이를 제거할 필요가 있고 본 연구에서는 이동 평균을 이용하여 제거한 후 예측을 하였다. 1991 년부터 2005 년까지의 기상자료와 수량자료를 학습에 사용하였고 2006 년부터 2015 년까지의 자료들을 검증용으로 사용하였다. 학습자료에 대해서는 상당히 정확한 예측 능력을 보여주었으나 검증 자료에서는 그렇지 못하였다. 그 이유를 분석하기 위해 학습 자료와 검증자료에 대해서 각각 변수 중요도를 산출하여 비교한 결과 두 자료 간에 월별 기상 자료에 대한 중요도가 변동되었음을 발견하였다. 이러하 차이가 발생한 이유는 학습자료와 검증 자료에서의 전국적으로 표준이앙기가 이동하여 벼의 생육기간 자체가 변하였기 때문이다. 따라서, 정확한 예측을 위해서는 지역별 파종기 또는 이앙기에 대한 자료가 필요하며 단순히 기상 자료만을 활용한 예측은 어려운 것으로 생긱된다.

• 한국전자통신학회논문지
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• 제18권1호
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• pp.99-106
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• 2023

최근 딥러닝 기술을 활용하여 작물 생산량 예측 연구가 많이 진행되고 있다. 딥러닝 알고리즘은 입력 데이터 세트와 작물 예측 결과에 대한 선형 맵을 구성하는데 어려움이 있다. 또한, 알고리즘 구현은 획득한 속성의 비율에 긍정적으로 의존한다. 심층강화학습을 작물 생산량 예측 응용에 적용한다면 이러한 한계점을 보완할 수 있다. 본 논문은 작물 생산량 예측을 개선하기 위해 DQN, Double DQN 및 Dueling DQN 의 성능을 분석한다. DQN 알고리즘은 과대 평가 문제가 제기되지만, Double DQN은 과대 평가를 줄이고 더 나은 결과를 얻을 수 있다. 본 논문에서 제안된 모델은 거짓 판정을 줄이고 예측 정확도를 높이는 것으로 나타났다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Advances in nano research
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• 제14권3호
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• 한국잡초학회지
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• 제30권4호
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• pp.390-395
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• 2010

콩재배시 발생하고 있는 기생잡초인 미국실새삼의 발생밀도가 콩 수량에 미치는 영향을 정량화하고 이들 경합에 의한 콩의 피해를 예측하여 콩 재배시 효율적인 잡초방제체계 관리정보를 제공하기 위하여 수행한 연구결과를 요약하면 다음과 같다. 미국실새삼의 발생밀도가 높아지더라도 콩의 생육초기에는 경장과 분지수에는 크게 영향을 미치지 않았으나 생육후기로 갈수록 감소하는 경향을 나타내었고 식물체 건물중, 백립중, 협수에서 유의적으로 감소하는 경향을 보였으며 콩에 미치는 피해정도는 협수> 백립중> 건물중> 분지수> 경장 순으로 영향을 미치는 것을 알 수 있었다. 미국실새삼 경합밀도가 1~48본 $m^{-2}$일때 콩 수량은 각각 80.3~99.7%의 수량감소를 보였으며, 미국실새삼 경합밀도별로 조사된 콩의 수량 자료에 따른 콩 수량 예측 모델은 Y = 274.6783/(1+4.3522X), $R^2=0.999$였으며 50% 수량감소를 유발하는 미국실새삼의 잡초밀도는 $m^2$당 0.23개로 추정되어 콩 재배지에 발생시 심각하게 피해를 줄 잡초로 예상된다. 생산 및 증수비용을 고려한 콩밭 미국실새삼의 경제적 피해한계 밀도 수준은 $m^2$당 0.004개로 예측할 수 있었으며 이보다 발생밀도가 많을 경우에는 잡초를 방제하는 것이 경제적으로 유리할 것으로 사료된다.

• 한국유기농업학회지
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• 제25권2호
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• pp.279-293
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• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• 한국작물학회지
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• 제19권
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• pp.100-131
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• 1975

To obtain basic information on the improvement of naked barley production. and to clarify the relation-ships between yield or yield components and some meteorogical factors for yield prediction were the objectives of this study. The basic data used in this study were obtained from the experiments carried out for 16 years from 1958 to 1974 at the Chon-nam Provincial Office of Rural development. The simple correlation coefficients and multiple regression coefficients among the yield or yield components and meteorogical factors were calculated for the study. Days to emergence ranged from 8 to 26 days were reduced under conditions of mean minimum air temperature were high. The early emergence contributed to increasing plant height and number of tillers as well as to earlier maximum tillering and heading date. The plant height before wintering showed positive correlations with the hours of sunshine. On the other hand, plant height measured on march 1st and March 20th showed positive correlation with the amount of precipitation and negative correlation with the hours of sunshine during the wintering or regrowth stage. Kernel weights were affected by the hours of sunshine and rainfall after heading, and kernel weights were less variable when the hours of sunshine were relatively long and rainfalls in May were around 80 to 10mm. It seemed that grain yields were mostly affected by the climatic condition in March. showing the negative correlation between yield and mean air temperature, minimum air temperature during the period. In the other hand, the yield was shown to have positive correlation with hours of sunshine. Some yield prediction equations were obtained from the data of mean air temperature, mean minimum temperature and accumulated air temperature in March. Yield prediction was also possible by using multiple regression equations, which were derived from yield data and the number of spikes and plant height as observed at May 20th.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.

• 식물병연구
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• 제19권3호
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• pp.196-200
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• 2013

콩 점무늬병이 수량에 미치는 영향을 평가하고 경제적 방제수준을 설정하고자 본 시험을 수행하였다. 점무늬병 발병정도와 주당 협수, 주당 총립수, 주당 총립중, 등숙률, 100립중 및 수량과의 상관계수는 각각 -0.90, -0.90, -0.92, -0.99, -0.90, -0.94로 통계적으로 고도의 유의성을 나타내었다. 콩 점무늬병의 병반면적률이 증가됨에 따라 수량은 반비례하여 감소하였는데, 콩 점무늬병 발병정도(x)에 따른 수량(y) 예측모델을 산출한 결과, 회귀식은 y = -3.7213x + 354.99($R^2$= 0.9047)로 고도의 부의 상관이 있었다. 이 회귀식을 토대로 경제적 피해허용수준은 병반 면적률 3.3%, 경제적 방제수준(ET)은 병반면적율 2.6%로 설정할 수 있었다.

• 한국측량학회지
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• 제34권4호
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.