This study introduced a two-stage cultivation method for sweet pepper seedlings, integrating the strengths of a closed plant factory and solar greenhouse, to mitigate the environmental constraints in Northeast China d...This study introduced a two-stage cultivation method for sweet pepper seedlings, integrating the strengths of a closed plant factory and solar greenhouse, to mitigate the environmental constraints in Northeast China during the early spring season. In the first stage, seedlings were cultivated in a closed plant factory, followed by a second stage in a solar greenhouse. Four treatments- T1 (9 and 36 d), T2 (12 and 33 d), T3 (15 and 30 d), and T4 (18 and 27 d) - were designed for the first and second stages, respectively, with solar greenhouse-only approach serving as the control (CK). The findings reveal that the two-stage methodology significantly outperformed the control across multiple metrics, including seedling health index, chlorophyll content, photosynthetic capacity, yield, etc. Specifically, T3 emerged as optimal, boosting the health index by 38.59%, elevating chlorophyll content by 39.61%, increasing net photosynthesis by 34.61%, and augmenting yield per plant by 40.67%. Additionally, T3 expedited the time to harvest by 25 d compared to the control. Although the seedling cost for T3 was 0.12 RMB yuan higher, the benefits offset the additional investment. In conclusion, the two-stage cultivation method effectively leverages the advantages of both closed-plant factories and solar greenhouses, resulting in superior seedling quality compared to using only solar greenhouses. It offers a practical and economically viable solution for enhancing the quality and yield of sweet pepper seedlings, thus contributing to the progress in the field of facility seedling cultivation research.展开更多
In this study,for the first time a suitable pesticide residue detection system for dandelion(Taraxacum officinale L.)was established based on electronic nose to determine and study the concentration of pesticide resid...In this study,for the first time a suitable pesticide residue detection system for dandelion(Taraxacum officinale L.)was established based on electronic nose to determine and study the concentration of pesticide residue in dandelion.Dandelions were sprayed with different concentrations of pesticides(avermectin,trichlorfon,deltamethrin,and acetamiprid),respectively.Data collection was performed by application of an electronic nose equipped with 12 metal oxide semiconductor(MOS)sensors.Data analysis was conducted using different methods including BP neural network and random forest(RF)as well as the support vector machine(SVM).The results showed the superior effectiveness of SVM in discrimination and classification of non-exceeding maximum residue limits(MRLs)and exceeding MRLs standards.Moreover,the model trained by SVM has the best performance for the classification of pesticide categories in dandelion,and the classification accuracy was 91.7%.The results of this study can provide reference for further development and construction of efficient detection technology of pesticide residues based on electronic nose for agricultural products.展开更多
基金supported by the China Agricultural Research System of MOF and MARA (Grant No.CARS-24-G-05)Jilin Province Science and Technology Development Plan Talent Special Project (Grant No.232695HJ0101110676).
文摘This study introduced a two-stage cultivation method for sweet pepper seedlings, integrating the strengths of a closed plant factory and solar greenhouse, to mitigate the environmental constraints in Northeast China during the early spring season. In the first stage, seedlings were cultivated in a closed plant factory, followed by a second stage in a solar greenhouse. Four treatments- T1 (9 and 36 d), T2 (12 and 33 d), T3 (15 and 30 d), and T4 (18 and 27 d) - were designed for the first and second stages, respectively, with solar greenhouse-only approach serving as the control (CK). The findings reveal that the two-stage methodology significantly outperformed the control across multiple metrics, including seedling health index, chlorophyll content, photosynthetic capacity, yield, etc. Specifically, T3 emerged as optimal, boosting the health index by 38.59%, elevating chlorophyll content by 39.61%, increasing net photosynthesis by 34.61%, and augmenting yield per plant by 40.67%. Additionally, T3 expedited the time to harvest by 25 d compared to the control. Although the seedling cost for T3 was 0.12 RMB yuan higher, the benefits offset the additional investment. In conclusion, the two-stage cultivation method effectively leverages the advantages of both closed-plant factories and solar greenhouses, resulting in superior seedling quality compared to using only solar greenhouses. It offers a practical and economically viable solution for enhancing the quality and yield of sweet pepper seedlings, thus contributing to the progress in the field of facility seedling cultivation research.
基金supported by the National Natural Science Found of China(Grant No.51875245)the Science-Technology Development Plan Project of Jilin Province(Grant No.20210203099SF+4 种基金No.20210203004SF)the“13th Five-Year Plan”Scientific Research Foundation of the Education Department of Jilin Province(Grant No.JJKH20200871KJNo.JJKH20200870KJNo.JJKH20200334KJNo.JJKH20210338KJ).
文摘In this study,for the first time a suitable pesticide residue detection system for dandelion(Taraxacum officinale L.)was established based on electronic nose to determine and study the concentration of pesticide residue in dandelion.Dandelions were sprayed with different concentrations of pesticides(avermectin,trichlorfon,deltamethrin,and acetamiprid),respectively.Data collection was performed by application of an electronic nose equipped with 12 metal oxide semiconductor(MOS)sensors.Data analysis was conducted using different methods including BP neural network and random forest(RF)as well as the support vector machine(SVM).The results showed the superior effectiveness of SVM in discrimination and classification of non-exceeding maximum residue limits(MRLs)and exceeding MRLs standards.Moreover,the model trained by SVM has the best performance for the classification of pesticide categories in dandelion,and the classification accuracy was 91.7%.The results of this study can provide reference for further development and construction of efficient detection technology of pesticide residues based on electronic nose for agricultural products.
