Development of an Automated Management System for Agricultural Technologies in Horticulture

Khort, Dmitriy; Kutyrev, Alexey; Smirnov, Igor; Voronkov, I. V.

doi:10.22314/2073-7599-2021-15-2-61-68

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…In [112], an automated control system for agricultural technologies in industrial horticulture was developed with the possibility of conducting ground inspections using a mobile application. The system provides real-time processing of information flows reflecting the characteristics of the growth and condition of plants in critical phases of development.…”

Section: Crop Productionmentioning

confidence: 99%

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Matsveichuk¹,

Sotskov²

2023

Preprint

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Development of agriculture in Russia and Belarus is based on the practical implementation of "smart" systems in agriculture based on the use of modern wireless, intelligent technologies and Internet of Things. This review presents research articles (mainly, in Russian) published in the period of 2013 – 2022 on the use of cloud technologies and Internet of Things for the development of agriculture in Russia and Belarus. An analysis of the use of cloud technologies and Internet of Things in the modern world is given on the basis of research articles and reviews published in English in the period of 2017 – 2022. The main directions of digitalization of modern agriculture are listed. The uses of cloud technologies and Internet of Things in agriculture are described along with promising directions for further research and applications.

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Section: Crop Productionmentioning

confidence: 99%

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Matsveichuk¹,

Sotskov²

2023

Preprint

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Intelligent crop yield prediction system using neural networks and databases

Kutyrev,

Zubina

2024

BIO Web Conf.

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Machine learning plays an important role in decision support and yield forecasting. This is an effective tool for determining strategies during the growing season of plants. The article proposes a method for predicting yield using a complex system consisting of a convolutional neural network (CNN), a feedforward neural network (FNN), and a SQLiteStudio database. The system includes several stages of data processing, starting with the collection and analysis of images and digital data obtained from various sources, and ending with yield forecasting based on this data. A convolutional neural network (CNN) is used to analyze images and video streams to recognize and count fruits on trees, providing accurate data about the status of the crop. Feedforward neural network (FNN) is used to analyze digital data, such as weather station data and long-term crop yield data, to subsequently predict crop yields. The received data is stored in a relational database, which ensures their structured storage and access for subsequent processing. Used SQL language to perform various database operations. To automate the process of counting fruits on trees, the YOLOv8 convolutional neural network model is used, which allows recognizing objects in real time. A Python script has been developed to process images using YOLOv8 and save the results to a database. An integrated system combines various methods and technologies to predict yields and automate data collection and analysis processes. The developed model showed a mean square error (MSE) of 7.33 and a mean absolute percentage error (MAPE) of 6.27%.

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Intelligent Technologies and Robotic Machines for Garden Crops Cultivation

2021

Self Cite

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The existing models of industrial robots cannot perform technological processes of apple harvesting. It is noted that there is a need for developing special actuators, grippers and new control algorithms for harvesting horticulture products. (Research purpose) The research aimed to develop an intelligent control system for horticulture industrial technologies and robotic techniques for yield monitoring and fruit harvesting. (Materials and methods) The research methodology was based on such modern methods as computer modeling and programming. In particular, the following methods were applied: systems analysis, artificial neural networks theory, pattern recognition, digital signal processing. The development of software, hardware and software was carried out in accordance with the requirements of GOST technical standards. The following programming languages were used: (C / C ++)-based OpenCV library, Spyder Python Development Environment, PyTorch and Flask frameworks, and JavaScript. Image marking for training neural networks was carried out via VGG ImageAnnotator and in Labelbox. The design process was based on the finite element method, CAD SolidWorks software environment. (Results and discussion) An intelligent management system for horticulture industrial technologies has been created based the on the «Agrointellect VIM» hardware and software complex. The concept of the system is shown to be implemented via computer and communication technology, robotic machines, the software for collecting, organizing, analyzing and storing data. The gripper proves to fix an apple gently and holds it securely. Depending on the size, the fruit fixation time is 1.5-2.0 seconds, the fruit maximum size is 85 per 80 millimeters , and its maximum weight is 500 grams. (Conclusions) The developed intelligent control system for industrial technologies based on «Agrointellect VIM» hardware and software complex ensures the efficient real-time processing of information necessary for the design of intelligent agricultural technologies using robotic machines and artificial intelligence systems.

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Development of an Automated Management System for Agricultural Technologies in Horticulture

Cited by 6 publications

References 13 publications

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Digital Technologies, Internet of Things and Cloud Computations Used in Agriculture: Surveys and Literature in Russian

Intelligent crop yield prediction system using neural networks and databases

Intelligent Technologies and Robotic Machines for Garden Crops Cultivation

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