The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was de...The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.展开更多
A series of novel N-allyloxy/propargyloxy aryloxyphenoxy propionamide compounds was designed and prepared. The structures of the synthesized compounds were confirmed by means of 1H NMR, 13C NMR, LC-MS, elemental analy...A series of novel N-allyloxy/propargyloxy aryloxyphenoxy propionamide compounds was designed and prepared. The structures of the synthesized compounds were confirmed by means of 1H NMR, 13C NMR, LC-MS, elemental analysis and IR. The bioassay results indicate that when against Digitaria sanguinalis and Echinochloa crus-galli, （R）-N-（propargyloxy）-2-{4-[（6-chloroquinoxalin-2-yl） oxy] phenoxy}propanamide（1m）（IC50=6.8 and 6.5 g/hm2, respectively） and （R）-N-（allyloxy）-2-{4-[（6-chloroquinoxalin-2-yl） oxy]phenoxy}propanamide（1r）（IC50=7.4 and 6.0 g/hm2, respectively） are much more effective than commercial aryloxyphenoxypropionic ester herbicide clodinafop-propargyl（IC50=46.5 and 14.6 g/hm2, respectively）. The results of crop selectivity show that compounds 1m and 1r are safe to soybean, rape and cotton and can be used as herbicides for soybean, rape and cotton crop.展开更多
文摘The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.
基金Supported by the National Natural Science Foundation of China（No.21272062）, the Startup Foundation of China Three Gorges University（No.KJ2014B084） and the Open Foundation of Hubei Key Laboratory of Tumor Microenvironment and Im- munotherapy, China（No.2015KZL09）.
文摘A series of novel N-allyloxy/propargyloxy aryloxyphenoxy propionamide compounds was designed and prepared. The structures of the synthesized compounds were confirmed by means of 1H NMR, 13C NMR, LC-MS, elemental analysis and IR. The bioassay results indicate that when against Digitaria sanguinalis and Echinochloa crus-galli, （R）-N-（propargyloxy）-2-{4-[（6-chloroquinoxalin-2-yl） oxy] phenoxy}propanamide（1m）（IC50=6.8 and 6.5 g/hm2, respectively） and （R）-N-（allyloxy）-2-{4-[（6-chloroquinoxalin-2-yl） oxy]phenoxy}propanamide（1r）（IC50=7.4 and 6.0 g/hm2, respectively） are much more effective than commercial aryloxyphenoxypropionic ester herbicide clodinafop-propargyl（IC50=46.5 and 14.6 g/hm2, respectively）. The results of crop selectivity show that compounds 1m and 1r are safe to soybean, rape and cotton and can be used as herbicides for soybean, rape and cotton crop.
