Macadamia has the highest economic value and has always enjoyed the reputation of"king of dried fruits".In the process of digital management,irrigation is the most widely used.This paper designs the automati...Macadamia has the highest economic value and has always enjoyed the reputation of"king of dried fruits".In the process of digital management,irrigation is the most widely used.This paper designs the automatic control drip irrigation device in the digital management of macadamia nuts,including drip irrigation pipe,soil moisture sensor,motor,water storage chamber,water pump and central controller,and then gives an implementation scheme.The device is not only convenient for laying and positioning,but also convenient for controlling the overall irrigation water consumption during each drip irrigation and accurate use and improving the level of digital management.展开更多
An internet of things-based subsoiling operation monitoring system for agricultural machinery is able to identify the type and operating state of a certain machinery by collecting and recognizing its images;however,it...An internet of things-based subsoiling operation monitoring system for agricultural machinery is able to identify the type and operating state of a certain machinery by collecting and recognizing its images;however,it does not meet regulatory requirements due to a large image data volume,heavy workload by artificial selective examination,and low efficiency.In this study,a dataset containing machinery images of over 100 machines was established,which including subsoilers,rotary cultivators,reversible plows,subsoiling and soil-preparation machines,seeders,and non-machinery images.The images were annotated in tensorflow,a deep learning platform from Google.Then,a convolutional neural network(CNN)was designed for targeting actual regulatory demands and image characteristics,which was optimized by reducing overfitting and improving training efficiency.Model training results showed that the recognition rate of this machinery recognition network to the demonstration dataset reached 98.5%.In comparison,the recognition rates of LeNet and AlexNet under the same conditions were 81%and 98.8%,respectively.In terms of model recognition efficiency,it took AlexNet 60 h to complete training and 0.3 s to recognize 1 image,whereas the proposed machinery recognition network took only half that time to complete training and 0.1 s to recognize 1 image.To further verify the practicability of this model,6 types of images,with 200 images in each type,were randomly selected and used for testing;results indicated that the average recognition recall rate of various types of machinery images was 98.8%.In addition,the model was robust to illumination,environmental changes,and small-area occlusion,and thus was competent for intelligent image recognition of subsoiling operation monitoring systems.展开更多
This paper mainly introduces the design and research of plant cultivation device in intelligent agricultural greenhouse.The device can automatically ventilate and control the temperature when the carbon dioxide conten...This paper mainly introduces the design and research of plant cultivation device in intelligent agricultural greenhouse.The device can automatically ventilate and control the temperature when the carbon dioxide content is insufficient.At the same time,the light color can be selected according to the plant properties,and the light and dark time can be controlled to provide a suitable growth environment for plants,which makes up for the shortcomings of the existing ordinary greenhouse.With China's strong support for agriculture,automatic agriculture is developing rapidly,and has a broad market prospect.展开更多
基金Supported by Guangxi Science and Technology Program Project of China(Guike AD19245169)Yulin City Scientific Research and Technology Development Plan Project(Yushike 20202001,2019Cxpt00A4,Yushike 20204038).
文摘Macadamia has the highest economic value and has always enjoyed the reputation of"king of dried fruits".In the process of digital management,irrigation is the most widely used.This paper designs the automatic control drip irrigation device in the digital management of macadamia nuts,including drip irrigation pipe,soil moisture sensor,motor,water storage chamber,water pump and central controller,and then gives an implementation scheme.The device is not only convenient for laying and positioning,but also convenient for controlling the overall irrigation water consumption during each drip irrigation and accurate use and improving the level of digital management.
基金This study was financially supported by National Nature Science Foundation of China(Grant No.31571563 and 31571564)The National Key Research and Development Program of China(Grant No.2016YFD0700303)。
文摘An internet of things-based subsoiling operation monitoring system for agricultural machinery is able to identify the type and operating state of a certain machinery by collecting and recognizing its images;however,it does not meet regulatory requirements due to a large image data volume,heavy workload by artificial selective examination,and low efficiency.In this study,a dataset containing machinery images of over 100 machines was established,which including subsoilers,rotary cultivators,reversible plows,subsoiling and soil-preparation machines,seeders,and non-machinery images.The images were annotated in tensorflow,a deep learning platform from Google.Then,a convolutional neural network(CNN)was designed for targeting actual regulatory demands and image characteristics,which was optimized by reducing overfitting and improving training efficiency.Model training results showed that the recognition rate of this machinery recognition network to the demonstration dataset reached 98.5%.In comparison,the recognition rates of LeNet and AlexNet under the same conditions were 81%and 98.8%,respectively.In terms of model recognition efficiency,it took AlexNet 60 h to complete training and 0.3 s to recognize 1 image,whereas the proposed machinery recognition network took only half that time to complete training and 0.1 s to recognize 1 image.To further verify the practicability of this model,6 types of images,with 200 images in each type,were randomly selected and used for testing;results indicated that the average recognition recall rate of various types of machinery images was 98.8%.In addition,the model was robust to illumination,environmental changes,and small-area occlusion,and thus was competent for intelligent image recognition of subsoiling operation monitoring systems.
文摘This paper mainly introduces the design and research of plant cultivation device in intelligent agricultural greenhouse.The device can automatically ventilate and control the temperature when the carbon dioxide content is insufficient.At the same time,the light color can be selected according to the plant properties,and the light and dark time can be controlled to provide a suitable growth environment for plants,which makes up for the shortcomings of the existing ordinary greenhouse.With China's strong support for agriculture,automatic agriculture is developing rapidly,and has a broad market prospect.
