By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant ...By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.展开更多
The main diseases and pests in the major growing area of jujube in Shanxi Province in recent years are investigated and studied,and several main diseases and pests are described.Based on the green prevention and contr...The main diseases and pests in the major growing area of jujube in Shanxi Province in recent years are investigated and studied,and several main diseases and pests are described.Based on the green prevention and control concept of crop diseases and pests proposed by the Ministry of Agriculture of China,the prevention and control of jujube diseases and pests are expounded from the perspectives of strengthening forecast,agricultural management,biological control and chemical control,in order to provide scientific basis for green development of jujube industry.展开更多
In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot le...In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.展开更多
基金Supported by a Grant from the Science and Technology Project ofYunnan Province(2006NG02)~~
文摘By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.
基金Sponsored by Young and Middle-aged Innovative Talents Training Program of Universities and Colleges in Tianjin (J01009030709)Enterprise Science and Technology Commissioner Project of Tianjin (20YDTPJC01330)+1 种基金Agricultural Science and Technology Project of Baodi District (201918)Cultivation Project of Hetian Polytechnic Horizontal Joint Project of Yisheng Orchard in Hetian City。
文摘The main diseases and pests in the major growing area of jujube in Shanxi Province in recent years are investigated and studied,and several main diseases and pests are described.Based on the green prevention and control concept of crop diseases and pests proposed by the Ministry of Agriculture of China,the prevention and control of jujube diseases and pests are expounded from the perspectives of strengthening forecast,agricultural management,biological control and chemical control,in order to provide scientific basis for green development of jujube industry.
基金R.A.Krohling thanks the Brazilian research agency Conselho Nacional de Desenvolvimento Científico e Tecnólogico(CNPq)for financial support under grant no.304688/2021-5.
文摘In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.
