Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applic...Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.展开更多
The optimum rate and application timing of Nitrogen(N)fertilizer are crucial in achieving a high yield in rice cultivation;however,conventional laboratory testing of plant nutrients is time-consuming and expensive.To ...The optimum rate and application timing of Nitrogen(N)fertilizer are crucial in achieving a high yield in rice cultivation;however,conventional laboratory testing of plant nutrients is time-consuming and expensive.To develop a site-specific spatial variable rate application method to overcome the limitations of traditional techniques,especially in fields under a double-cropping system,this study focused on the relationship between Soil Plant Analysis Development(SPAD)chlorophyll meter readings and N content in leaves during different growth stages to introduce the most suitable stage for assessment of crop N and prediction of rice yield.The SPAD readings and leaf N content were measured on the uppermost fully expanded leaf at panicle formation and booting stages.Grain yield was also measured at the end of the season.The analysis of variance,variogram,and kriging were calculated to determine the variability of attributes and their relationship,and finally,variability maps were created.Significant linear relationships were observed between attributes,with the same trends in different sampling dates;however,accuracy of semivariance estimation reduces with the growth stage.Results of the study also implied that there was a better relationship between rice leaf N content(R^2=0.93),as well as yield(R2=0.81),with SPAD readings at the panicle formation stage.Therefore,the SPAD-based evaluation of N status and prediction of rice yield is more reliable on this stage rather than at the booting stage.This study proved that the application of SPAD chlorophyll meter paves the way for real-time paddy N management and grain yield estimation.It can be reliably exploited in precision agriculture of paddy fields under double-cropping cultivation to understand and control spatial variations.展开更多
基金The authors wish to acknowledge the Ministry of Higher Education,Malaysia for financial support via the Transdisciplinary Research Grant Scheme Project(Grant No.TRGS/1/2020/UPM/02/7).
文摘Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.
基金the partially financial support of the Ministry of Education,Youth and Sport of the Czech Republic-projects‘CENAKVA’(project No.CZ.1.05/2.1.00/01.0024),‘CENAKVA II’(project No.LO1205 under the NPU I program).
文摘The optimum rate and application timing of Nitrogen(N)fertilizer are crucial in achieving a high yield in rice cultivation;however,conventional laboratory testing of plant nutrients is time-consuming and expensive.To develop a site-specific spatial variable rate application method to overcome the limitations of traditional techniques,especially in fields under a double-cropping system,this study focused on the relationship between Soil Plant Analysis Development(SPAD)chlorophyll meter readings and N content in leaves during different growth stages to introduce the most suitable stage for assessment of crop N and prediction of rice yield.The SPAD readings and leaf N content were measured on the uppermost fully expanded leaf at panicle formation and booting stages.Grain yield was also measured at the end of the season.The analysis of variance,variogram,and kriging were calculated to determine the variability of attributes and their relationship,and finally,variability maps were created.Significant linear relationships were observed between attributes,with the same trends in different sampling dates;however,accuracy of semivariance estimation reduces with the growth stage.Results of the study also implied that there was a better relationship between rice leaf N content(R^2=0.93),as well as yield(R2=0.81),with SPAD readings at the panicle formation stage.Therefore,the SPAD-based evaluation of N status and prediction of rice yield is more reliable on this stage rather than at the booting stage.This study proved that the application of SPAD chlorophyll meter paves the way for real-time paddy N management and grain yield estimation.It can be reliably exploited in precision agriculture of paddy fields under double-cropping cultivation to understand and control spatial variations.
