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
基于华北地区44个气象站点1981-2020年气象、土壤和作物数据,利用农业生产系统模型(Agricultural Production Systems sIMulator,APSIM)模拟华北春玉米一年一熟和冬小麦-夏玉米一年两熟的产量、CO_(2)和N2O排放量,解析与春玉米一年一熟...基于华北地区44个气象站点1981-2020年气象、土壤和作物数据,利用农业生产系统模型(Agricultural Production Systems sIMulator,APSIM)模拟华北春玉米一年一熟和冬小麦-夏玉米一年两熟的产量、CO_(2)和N2O排放量,解析与春玉米一年一熟种植制度相比冬小麦-夏玉米一年两熟种植制度在周年产量提高的同时温室气体排放量的变化;在此基础上,利用APSIM模型模拟不同水氮管理措施下冬小麦-夏玉米产量和温室气体排放量,并计算氮肥农学效率和水分生产力,采取归一化方法明确各处理达到高产、高效和低排多目标协同效果的主体种植制度应对气候变化的水氮智慧管理措施,以及定量气候变化背景下华北地区不同种植制度对温室气体排放量和排放强度的影响程度,明确不同水氮管理措施下周年产量、资源利用效率和温室气体排放量的空间差异性,为华北主体种植模式智慧应对气候变化提供科学依据。结果表明:(1)1981-2020年研究区域春玉米、冬小麦-夏玉米周年单位面积温室气体排放量分别为0.48×10^(4)~1.65×10^(4)kg CO_(2)-eq·hm^(-2)和2.36×10^(4)~4.11×10^(4)kg CO_(2)-eq·hm^(-2),冬小麦-夏玉米较春玉米温室气体排放量增加了406.7%;(2)1981-2020年研究区域春玉米、冬小麦-夏玉米温室气体排放强度分别为0.08~0.35kg CO_(2)-eq·kg^(-1)和0.19~0.47kg CO_(2)-eq·kg^(-1),冬小麦-夏玉米较春玉米增加了153.8%;(3)随着冬小麦灌溉量增加,冬小麦-夏玉米周年产量和温室气体排放量均呈增加趋势,且灌溉时期对周年产量和温室气体排放量无明显影响;(4)各作物氮肥施用量在0~225kg·hm^(-2)区间时,冬小麦-夏玉米的产量和温室气体排放量随着施氮量的增加而明显增加;施氮量达到225kg·hm^(-2)之后随着施氮量增加周年产量无显著变化,但温室气体排放量显著增加。针对冬小麦-夏玉米一年两熟种植制度采取适宜的灌溉模式和施氮量,可实现周年较高产量且温室气体排放量相对较低。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.
文摘基于华北地区44个气象站点1981-2020年气象、土壤和作物数据,利用农业生产系统模型(Agricultural Production Systems sIMulator,APSIM)模拟华北春玉米一年一熟和冬小麦-夏玉米一年两熟的产量、CO_(2)和N2O排放量,解析与春玉米一年一熟种植制度相比冬小麦-夏玉米一年两熟种植制度在周年产量提高的同时温室气体排放量的变化;在此基础上,利用APSIM模型模拟不同水氮管理措施下冬小麦-夏玉米产量和温室气体排放量,并计算氮肥农学效率和水分生产力,采取归一化方法明确各处理达到高产、高效和低排多目标协同效果的主体种植制度应对气候变化的水氮智慧管理措施,以及定量气候变化背景下华北地区不同种植制度对温室气体排放量和排放强度的影响程度,明确不同水氮管理措施下周年产量、资源利用效率和温室气体排放量的空间差异性,为华北主体种植模式智慧应对气候变化提供科学依据。结果表明:(1)1981-2020年研究区域春玉米、冬小麦-夏玉米周年单位面积温室气体排放量分别为0.48×10^(4)~1.65×10^(4)kg CO_(2)-eq·hm^(-2)和2.36×10^(4)~4.11×10^(4)kg CO_(2)-eq·hm^(-2),冬小麦-夏玉米较春玉米温室气体排放量增加了406.7%;(2)1981-2020年研究区域春玉米、冬小麦-夏玉米温室气体排放强度分别为0.08~0.35kg CO_(2)-eq·kg^(-1)和0.19~0.47kg CO_(2)-eq·kg^(-1),冬小麦-夏玉米较春玉米增加了153.8%;(3)随着冬小麦灌溉量增加,冬小麦-夏玉米周年产量和温室气体排放量均呈增加趋势,且灌溉时期对周年产量和温室气体排放量无明显影响;(4)各作物氮肥施用量在0~225kg·hm^(-2)区间时,冬小麦-夏玉米的产量和温室气体排放量随着施氮量的增加而明显增加;施氮量达到225kg·hm^(-2)之后随着施氮量增加周年产量无显著变化,但温室气体排放量显著增加。针对冬小麦-夏玉米一年两熟种植制度采取适宜的灌溉模式和施氮量,可实现周年较高产量且温室气体排放量相对较低。
