Growth-related traits,such as aboveground biomass and leaf area,are critical indicators to characterize the growth of greenhouse lettuce.Currently,nondestructive methods for estimating growth-related traits are subjec...Growth-related traits,such as aboveground biomass and leaf area,are critical indicators to characterize the growth of greenhouse lettuce.Currently,nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features.In this study,a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network(CNN).Taking lettuce images as the input,a CNN model was trained to learn the relationship between images and the corresponding growth-related traits,i.e.,leaf fresh weight(LFW),leaf dry weight(LDW),and leaf area(LA).To compare the results of the CNN model,widely adopted methods were also used.The results showed that the values estimated by CNN had good agreement with the actual measurements,with R^(2) values of 0.8938,0.8910,and 0.9156 and normalized root mean square error(NRMSE)values of 26.00,22.07,and 19.94%,outperforming the compared methods for all three growth-related traits.The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno.Generalization tests were conducted by using images of Tiberius from another growing season.The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits,with R2 values of 0.9277,0.9126,and 0.9251 and NRMSE values of 22.96,37.29,and 27.60%.The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.展开更多
In order to explore the effects of farmland quality evaluation on site selection of land consolidation projects,the methods of agricultural land use classification were used. Taking several project areas in Yanjin Cou...In order to explore the effects of farmland quality evaluation on site selection of land consolidation projects,the methods of agricultural land use classification were used. Taking several project areas in Yanjin County of Henan Province as examples,the farmland quality before and after land consolidation projects was evaluated. The results indicate that under the conditions of reasonable site selection,the implementation of land consolidation projects can effectively improve the farmland quality and increase the grain productivity. Therefore,before the site selection for land consolidation projects,it is recommended to carry out pre-evaluation of farmland quality,and guide the project implementation according to the evaluation results. Besides,it is recommended to focus on medium and low level farmland with large centralized area,excellent original production conditions,but low level of development and use.展开更多
Maximizing profit is usually the objective of optimal control of greenhouse cultivation.However,due to the problem of“the curse of dimensionality”,the global optimization of greenhouse climate is usually difficult w...Maximizing profit is usually the objective of optimal control of greenhouse cultivation.However,due to the problem of“the curse of dimensionality”,the global optimization of greenhouse climate is usually difficult when faced with a complex dynamic model and a long cultivation period.Compared with leafy vegetables with a much simpler dynamic model and a much shorter cultivation period,the year-round tomato model usually has many more states to describe its dynamics better.To solve the year-round climate control of greenhouse tomato cultivation,a rule-based model predictive control(MPC)algorithm is raised.The innovation of this paper lies in that the setpoints of the proposed MPC algorithms are determined by the external weather and the month-averaged predictions of the tomato price.With the greenhouse climate–tomato growth dynamic model and the economic performance index,different MPC algorithms are compared with the traditional on/off control algorithm and the open field cultivation.Quantified results of yield,cost,and profit are obtained with the weather data and market data collected in Beijing.Findings of this paper showed that the year-round greenhouse tomato cultivation in Beijing is hardly profitable with the tomato price sold as an open field product(XFD price).With the tomato price sold as a high-tech greenhouse product(JD price),the higher yield guarantees a higher profit.Moreover,the simple emphasis on energy minimization cannot even guarantee a higher yield than that in the open field.A synthetical consideration of yield and cost is a prerequisite for a high profit.展开更多
Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disea...Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper.The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure.Firstly,a coarse decision tree was built by the CART(Classification and Regression Tree)algorithm with a feature subset.The feature subset consisted of color features that was selected by Pearson’s Rank correlations.Then,the coarse decision tree was optimized by pruning.Using the optimized decision tree,segmentation of disease symptom images was achieved by conducting pixel-wise classification.In order to evaluate the robustness and accuracy of the proposed method,an experiment was performed using greenhouse cucumber downy mildew images.Results showed that the proposed method achieved an overall accuracy of 90.67%,indicating that the method was able to obtain robust segmentation of disease symptom images.展开更多
基金supported by the Beijing Leafy Vegetables Innovation Team of Modern Agro-industry Technology Research System(BAIC07-2020)the National Key Research and Development Project of Shandong(2017CXGC0201).
文摘Growth-related traits,such as aboveground biomass and leaf area,are critical indicators to characterize the growth of greenhouse lettuce.Currently,nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features.In this study,a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network(CNN).Taking lettuce images as the input,a CNN model was trained to learn the relationship between images and the corresponding growth-related traits,i.e.,leaf fresh weight(LFW),leaf dry weight(LDW),and leaf area(LA).To compare the results of the CNN model,widely adopted methods were also used.The results showed that the values estimated by CNN had good agreement with the actual measurements,with R^(2) values of 0.8938,0.8910,and 0.9156 and normalized root mean square error(NRMSE)values of 26.00,22.07,and 19.94%,outperforming the compared methods for all three growth-related traits.The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno.Generalization tests were conducted by using images of Tiberius from another growing season.The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits,with R2 values of 0.9277,0.9126,and 0.9251 and NRMSE values of 22.96,37.29,and 27.60%.The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.
基金Supported by Research on Theory and Techniques for Calculating Comprehensive Land Price in Land Requisition Areas
文摘In order to explore the effects of farmland quality evaluation on site selection of land consolidation projects,the methods of agricultural land use classification were used. Taking several project areas in Yanjin County of Henan Province as examples,the farmland quality before and after land consolidation projects was evaluated. The results indicate that under the conditions of reasonable site selection,the implementation of land consolidation projects can effectively improve the farmland quality and increase the grain productivity. Therefore,before the site selection for land consolidation projects,it is recommended to carry out pre-evaluation of farmland quality,and guide the project implementation according to the evaluation results. Besides,it is recommended to focus on medium and low level farmland with large centralized area,excellent original production conditions,but low level of development and use.
基金supported by Key Technology Research and Development Program of Shandong(2022CXGC020708)National Natural Science Foundation of China(32371998 and U20A2020)+2 种基金National Modern Agricultural Technology System Construction Project(CARS-23-D02)Beijing Innovation Consortium of Agriculture Research System(BAIC01-2023)the 2115 Talent Development Program of China Agricultural University.
文摘Maximizing profit is usually the objective of optimal control of greenhouse cultivation.However,due to the problem of“the curse of dimensionality”,the global optimization of greenhouse climate is usually difficult when faced with a complex dynamic model and a long cultivation period.Compared with leafy vegetables with a much simpler dynamic model and a much shorter cultivation period,the year-round tomato model usually has many more states to describe its dynamics better.To solve the year-round climate control of greenhouse tomato cultivation,a rule-based model predictive control(MPC)algorithm is raised.The innovation of this paper lies in that the setpoints of the proposed MPC algorithms are determined by the external weather and the month-averaged predictions of the tomato price.With the greenhouse climate–tomato growth dynamic model and the economic performance index,different MPC algorithms are compared with the traditional on/off control algorithm and the open field cultivation.Quantified results of yield,cost,and profit are obtained with the weather data and market data collected in Beijing.Findings of this paper showed that the year-round greenhouse tomato cultivation in Beijing is hardly profitable with the tomato price sold as an open field product(XFD price).With the tomato price sold as a high-tech greenhouse product(JD price),the higher yield guarantees a higher profit.Moreover,the simple emphasis on energy minimization cannot even guarantee a higher yield than that in the open field.A synthetical consideration of yield and cost is a prerequisite for a high profit.
基金The authors would like to thank the financial support provided by The National Key Research and Development Program of China(2016YFD0300606,2017YFD0300402 and 2017YFD0300401).
文摘Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper.The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure.Firstly,a coarse decision tree was built by the CART(Classification and Regression Tree)algorithm with a feature subset.The feature subset consisted of color features that was selected by Pearson’s Rank correlations.Then,the coarse decision tree was optimized by pruning.Using the optimized decision tree,segmentation of disease symptom images was achieved by conducting pixel-wise classification.In order to evaluate the robustness and accuracy of the proposed method,an experiment was performed using greenhouse cucumber downy mildew images.Results showed that the proposed method achieved an overall accuracy of 90.67%,indicating that the method was able to obtain robust segmentation of disease symptom images.
