Analysis of Grain Storage Loss Based on Decision Tree Algorithm

Liu, Xueli; Li, Bingchan; Shen, Dongqin; Cao, Jie; Mao, Bo

doi:10.1016/j.procs.2017.11.351

Cited by 21 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the recent change in China's national grain supporting policy has further aggravated the downturn in maize price (Huang and Yang, 2017). The grain reserve in China had been growing steadily since 2000 due to the price support program (Liu et al, 2017) [1]. By 2013, the national grain storage reached its peak, and the cost of storing a huge amount of grain became enormously high.…”

Section: Background and Analytical Framework 21 Grain Price Decline In Chinamentioning

confidence: 99%

Price decline, land rental markets and grain production in the North China Plain

Wang

Fan

et al. 2020

CAER

View full text Add to dashboard Cite

PurposeThis study examines the heterogeneous correlations between rural farmers' land renting behavior and their grain production when they experienced a significant price decline.Design/methodology/approachWe used well-timed panel data obtained from a two-round survey held in 2013 and 2017 among 621 households in the North China Plain. The empirical analyses were conducted by using the pooled ordinary least squares (OLS) and fixed effects models.FindingsRural tenants were having heterogeneous responses in land renting behavior and agricultural production when there was a price decline. A group of optimistic tenants (as professional farmers) were more likely to enlarge the farm scale for grain production through land rental markets but decrease variable investment levels (and subsequently decreased productivity) to cope with price decline. In contrast, nonprofessional farmers (the other rural tenants) were rather pessimistic about market performance, and they significantly decreased their grain production area to cope the price decline, but there was no decrease in grain productivity through reducing variable inputs.Originality/valueThis study contributes to the extant literature on the relationship between farmers' land renting-in behavior and agricultural production. By dividing the tenants into professional and nonprofessional farmers, we argue that there is a significant heterogeneous correlation between rural tenants' land renting behavior and grain production when farmers experience a price decline.

show abstract

Section: Background and Analytical Framework 21 Grain Price Decline In Chinamentioning

confidence: 99%

Price decline, land rental markets and grain production in the North China Plain

Wang

Fan

et al. 2020

CAER

View full text Add to dashboard Cite

show abstract

“…Predictive modeling is a statistical technique that has been achieving noteworthy results in the context of postharvest grain management as an innovative way of data analysis (Liakos, Busato, Moshou, Pearson, & Bochtis, 2018;Liu, Li, Shen, Cao, & Mao, 2017;Martinez-Feria et al, 2019;Romero et al, 2013;Yu, Li, Shen, Cao, & Mao, 2017). In short, a predictive model consists one or more mathematical functions applied to a set of observed data that, with the help of statistical inference, is capable of generating predictions for what might happen to response variable(s) of interest as inputs change.…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to predict the dry matter loss of stored soybean grains (Glycine max L.)

Zeymer

Guzzo

Araújo

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

Predictive modeling is useful to estimate future events based on collected data and discipline-specific knowledge to guide decision making. Machine learning is one of the most frequently used methods to develop predictive models. There is limited literature in which machine learning techniques have been applied to soybeans' behavior during storage, and thus, the goal of this study was to test different model frameworks that estimate dry matter loss of soybeans utilizing data acquired from a dynamic grain respiration system with different levels of moisture content (12, 14, 18, and 22%, wet basisw.b.) and temperature (25, 30, and 35 C). Five different models were trained and tested with a 2,625 point data set, which was partitioned into a training set (90%) and a testing set (10%). In the training step, 10-fold cross-validation was used to choose the best hyperparameters for each of the models. All fitted models were evaluated using standard metrics of Root Mean Square Error (RMSE), coefficient of determination (R 2 ), dispersion of residual values, and a coefficient of performance. The Random Forest model performed best in terms of the distance between predicted and observed values, with three predicting variables. The use of predictive modeling in the recent Agriculture 4.0 scenario is a promising development for risk management and decision making in the context of grain and oilseed storage. Practical ApplicationsSoy is one of the most important crops in the world. The dry matter loss through respiration is a major problem during storage. The results of this study can aid researchers and the soybean industry in managing storage as a risk management and decision-making tool.

show abstract

“…Waheed et al [95] devised a CART algorithm for categorising hyper-spectral information of the corn plots into different classes based on water stress, weeds' existence, and nitrogen application rates. Xueli Liu et al [96] established a decision tree model for assessing grain loss due to various factors involved in grain storage. Bosma et al [97] discussed the decision tree model for estimating and modelling the decision-making process of the agriculturists on assimilating aquaculture into agronomy in Vietnam.…”

Section: Classification and Regression Treesmentioning

confidence: 99%

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Sivakumar

Prabadevi²,

Velvizhi³

et al. 2022

IoT Applications Computing

View full text Add to dashboard Cite

Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things.

show abstract

Analysis of Grain Storage Loss Based on Decision Tree Algorithm

Cited by 21 publications

References 5 publications

Price decline, land rental markets and grain production in the North China Plain

Price decline, land rental markets and grain production in the North China Plain

Machine learning algorithms to predict the dry matter loss of stored soybean grains (Glycine max L.)

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Contact Info

Product

Resources

About