Tropical fruit trees constitute important biological resources in the global agrobiodiversity context. Unlike the tropical fruit trees of American and Asian origin, indigenous fruit trees (IFT) of tropical Africa have...Tropical fruit trees constitute important biological resources in the global agrobiodiversity context. Unlike the tropical fruit trees of American and Asian origin, indigenous fruit trees (IFT) of tropical Africa have scarcely achieved the status of international recognition in commodity markets and research arena outside Africa. This paper presented a critical review of the status of IFT in the Tropical African sub-regions (of West Africa, Central Africa, East Africa, Southern Africa and the Indian Ocean Islands) in relation to the introduced naturalised fruit trees from tropical America and Asia, threats to the diversity and sustainable use of IFT, analysis of the opportunities and challenges of developing IFT, as well as targets for crop improvement of the rich IFT of Tropical Africa. Domestication programme via relevant vegetative propagation techniques for priority IFT of the sub-regions was examined and advocated, in addition to the adoption of complementary conservation strategies, including Field GeneBanks in the management of the continent’s IFT diversity.展开更多
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.展开更多
The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability a...The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.展开更多
文摘Tropical fruit trees constitute important biological resources in the global agrobiodiversity context. Unlike the tropical fruit trees of American and Asian origin, indigenous fruit trees (IFT) of tropical Africa have scarcely achieved the status of international recognition in commodity markets and research arena outside Africa. This paper presented a critical review of the status of IFT in the Tropical African sub-regions (of West Africa, Central Africa, East Africa, Southern Africa and the Indian Ocean Islands) in relation to the introduced naturalised fruit trees from tropical America and Asia, threats to the diversity and sustainable use of IFT, analysis of the opportunities and challenges of developing IFT, as well as targets for crop improvement of the rich IFT of Tropical Africa. Domestication programme via relevant vegetative propagation techniques for priority IFT of the sub-regions was examined and advocated, in addition to the adoption of complementary conservation strategies, including Field GeneBanks in the management of the continent’s IFT diversity.
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
基金the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.
