Precision Agriculture System Research Articles

Preface

International Conference on Agriculture, Environment and Food Security (AEFS) is a leading international conference, held by Faculty of Agriculture, Universitas Sumatera Utara, Indonesia, for presenting novel and application experiences in the fields of agriculture, environment and food security. It also serves to foster communication among scientist, researchers, scholars, resource manager, practitioners, students, policy makers and all those who interested in agricultural, environmental sciences and food security from all around the world.AEFS Conference has been held annually since 2017. The AEFS Conference deliberations for the last four years were on the following themes of Agriculture, Plantation and Livestock for World Food Security (AEFS 2017); Prospects and Challenges of Sustainable Agriculture to Improve Food Security and Environment (AEFS 2018); Challenges on Food Security and Sovereignty in the Industrial Era 4.0 (AEFS 2019); Innovation and Technologies on Agricultural and Environmental Sustainability Systems (AEFS 2020). Moreover, for the 5th AEFS 2021 the theme was focused on “Precision Agriculture Technologies and Farming Systems Toward Food Security and Sustainability” with 7 selected tracks including Agricultural Engineering, Agricultural Economic, Food Science and Technology, Animal Science, Marine and Fisheries Science, Environmental Science and Plant Science (Agronomy and Plantation, Plant Breeding, Biotechnology, Integrated Pest Management and Soil Science).List of Organizing Committee, Editorial and Reviewing, PIC Committee, AEFS 2021 Organizing Committee are available in this pdf.

Deep neural network based fruit identification and grading system for precision agriculture

Precision agriculture is a systematic scientific way of farming with an aim to increase yield in terms of quality and quantity, ensure profitability, sustainability, and protection of the environment through optimum use of resources. Fruit quality is associated with several factors such as freshness level, vitamin content, nutritional content, etc. Grading of fruits is an important process as it ensures exact characteristics and helps in regulating price as per quality. With an increased demand for quality fruits, automated fruit grading systems are required to support the farmers and to maintain a sound pricing scheme in the market. This paper presents an automated fruit grading scheme based on deep neural networks. A convolutional neural network structure is proposed and compared with transfer learning-based models for grading the fruit images in the ‘Kaggle’ dataset. Remarkable results are achieved with the proposed as well as with the 4 pre-trained models, i.e., ResNet-50, VGG-16 & 19, and Inception-ResNet V2. Further the results are also compared with that obtained from the support vector machine classifier. For comparison of all these models, the input data is same i.e., intensities of the pixels in the input image. A user interface demonstration module is generated to demonstrate how it can be useful to the farmers for sorting their products.

非接触施肥流量实时监测系统的设计与试验

Precision fertilization is one of the important links in the precision agricultural operation system. In recent years, the demand for precision fertilization in facility greenhouse has become more and more urgent. As such, this paper designs an accurate and practical fertilization on-line monitoring system. The system adopts capacitance conversion chip PCAP01 and SCM STM32F103C8T6 to build a capacitance detection circuit. The effective acquisition of differential capacitance sensor microcapacitor signal output under this system is realized. The response relationship between the mass flow and the sensor capacitance output was studied, and a linear fitting model of fertilization was established. According to the prediction, the model determination coefficient was 0.9997. The experiment shows that the system has good adaptability to ambient temperature. The average measurement error is 1.13%, which meets the technical requirements of flow measurement. The capacitive fertilization online measurement system provides an effective way for the on-line acquisition of fertilization flow. It is of great significance for the implementation of high-precision variable fertilization.

IoT Enabled Smart Irrigation and Cultivation Recommendation System for Precision Agriculture

Agriculture is a separate economic sector. In agriculture, India ranks second. Agriculturists stress the need of fertilization and crop rotation. Today's farmers are unable to produce the maximum amount of food due to technological advancements. The major focus should be on using and collaborating with emerging technology in agriculture to boost output. The Internet of Things (IoT) helps estimate crop yields and other factors that contribute to high productivity. Soil temperature, pH, and water level all play a role in delivering optimal crops and increasing productivity. This study proposes "ACRIS: Agriculture Cultivation Recommender and Smart Irrigation System" to help farmers who use IoT in precision agriculture to increase crop output. The ACRIS system has three modules. "Accurate Farming Recommendations Using an Agriculture Factor-based Relevance Vector Analysis Model" is the first ACRIS module. Because of this, our model offers a more favorable situation based on relevant vector analysis. The second module is "AISM System: Advanced Irrigation Planner for Precision Farmers." The module forecasts soil moisture and organizes irrigation for farmers using precision agriculture to decrease water usage and boost productivity. The "AMOP System: ACRIS Multiparameter Optimization Systems for Precision Agriculture" module compares water content at different phases of plant development and integrates IoT technologies into agriculture to ensure optimal crop growth and water stability. The agricultural production is enormous, and it is vital to farmers' income.

Internet of Things (IoT) Device Management in Rural Areas to Support Precision Agriculture

Internet of Things (IoT) devices have an important role in supporting precision agriculture systems, namely acting as the first layer consisting of several nodes in a limited network group as a producer of land, environmental and plant parameter data that will be processed at the next level. This node is an embedded system consisting of sensors, microcontrollers, power supplies and wireless communication media. The challenge that arises is when the number of new nodes that will be connected to the concentrator in a limited network group is quite large and is still done manually by the user, so that it has the potential to cause complexity and errors in configuration. The purpose of this research is to design a self-registration scheme from each new node to the concentrator in a restricted network group automatically. The method used by new nodes for self-registration is to read the RSSI (Received Signal Strength Indicator) value from the concentrator, then exchange metadata according to the OCF (Open Connectivity Foundation) standard. The concentrator will refer to the maximum number of nodes allowed to join the group and check the metadata of the new node. If the registration process is successful, the new node will gain access rights to communicate with the concentrator and its closest neighbours in the network group. This mechanism will help farmers in rural areas to be able to configure Internet of Things (IoT) devices easily and provide benefits in supporting precision agriculture.

Experience and problems of implementation of digital solutions in animal husbandry of the Republic of Belarus (review)

The presented review article describes a number of some important software products, information systems, and other digital solutions that are extremely necessary for the effective functioning of the agro-industrial complex of the Republic of Belarus in general and animal husbandry in particular. The important global trends in the development of modern agriculture are considered, the benefits received by farmers from the use of various digital technologies are discussed, and the need to accelerate the current pace of introduction of diffirent elements of precision farming and animal husbandry systems into Belarusian agriculture is noted. In the animal husbandry of the republic a number of farms using Smart Farm technology already use robotic milking modules with monitoring of milk quality and the physiological state of animals, which reduces the incidence of cows with mastitis by 25–30 %, increases the time of economic use of animals up to 4–5 lactations. The use of robotic tools for the preparation and distribution of feed mixtures allows us to increase the milk yield of cows by 30–40 %. At the same time, the profitability of products produced using this technology can exceed 40 %. Feed preparation and distribution systems have been developed. On farms with free-stall housing animal husbandry, concentrated feed is normalized by the automated cow feeding station SKA-25 developed by RUE “NPC of the National Academy of Sciences of Belarus for Agricultural Mechanization”. The use of a feeding station makes it possible to automate the process of issuing concentrated feed, as well as to monitor the use of high-energy feed through automated process control systems on cattle farms.

Experience and problems of implementing digital solutions, elements of precision farming and animal husbandry in agriculture of the Republic of Belarus

The presented review article describes a number of some important developed, implemented and already used software products, information systems, and other digital solutions that are essential for the effective functioning of the agro-industrial complex of the republic. Important global trends in the development of modern agriculture are considered, the benefits received by farmers from the use of various digital technologies are discussed, the need to accelerate the current pace of introduction of various elements of precision farming and livestock systems into Belarusian agriculture is noted.

Low-cost on-line monitoring system for agriculture based on raspberry pi zero

Based on data from the National Defense Agency, the trend of conversion of agricultural land in the 1990s reached around 30,000 ha/year. However, in 2011 there was an increase of 110,000 ha/year and reached 150,000 ha/year in 2019. In fact, with the growing human population, the need for food is increasing from year to year. On the other hand, the slow regeneration of farmers and the lack of dissemination & technological innovation to increase agricultural productivity are challenges in realizing food security. In line with the ongoing industrial revolution in the industrial era 4.0, it has brought very significant changes, not only the type of technology that has shifted, but more importantly a change in mindset in this industrial era. This has a real impact on the direction of national development and has an impact on the agricultural system. The agricultural system is no longer just a cultivation activity, but agriculture is also part of the industrial system which is characterized by a transformation, both in terms of raw materials, economy, social and environment. The development of modern agriculture is marked by mindset changing of using biological and non-biological resources by the needs. The precise use of resources in agricultural production systems is then called precision agriculture. Precision agriculture is an agricultural concept with a systems approach towards low-input, high-efficiency, and sustainable agriculture. In a previous study, a CNC-based precision farming system (Farming-BOT) was developed. The device could perform vegetable crop cultivation automatically. To properly observe the development of cultivation, an online monitoring device based on the minicomputer was developed. The Raspberry Pi Zero, equipped with a 5MP camera and a temperature & humidity sensor, is used. The device is placed on the top of the Farming-BOT device. Image data, temperature, and humidity are sent to the cloud database periodically every day. Users can access the data through the website and Android application. Based on the testing result, with a stable internet connection, data on environmental conditions can be monitored perfectly.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Technical and technological foundations of soil cultivation in conditions of climate change

Purpose. To determine promising directions for the development of the technical and technological foundations of soil cultivation in the conditions of climate change. Methods. Monographic, abstract-logical, graph-analytical, induction and deduction, regression analysis of results. Results. Modern soil cultivation technologies are considered, their advantages and disadvantages, development trends are defined. It has been established that climate change will significantly affect the terms of execution of works, the conditions of interaction of working bodies of technical means with the soil environment. The parabolic relationship between traction resistance and soil moisture means that climate change will increase fuel costs for tillage. Therefore, the further search for new technical and technological solutions should be aimed at reducing energy consumption and preventing physical degradation of soils, substantiating structural and functional schemes of technical means for soil cultivation, parameters of working bodies and modes of operation, under which the number of dust particles in the surface layer of the soil will be minimal. Conclusions. The main direction of the development of the technical and technological base for the production of plant products in the conditions of climate change is the harmonization of the interaction of the working bodies of tillage machines with the soil, which will ensure the reduction of physical soil degradation and energy consumption to create a favorable environment for plant development and crop formation. In the conditions of global climate change, agricultural production is being reoriented towards precision farming systems, therefore, crop production technologies should be based on targeted tillage with the simultaneous introduction of a full dose of mineral nutrition into the seed sowing area for the planned harvest, sowing of seeds and preservation of plant residues on the surface of the soil for protection it from overheating and loss of moisture. Keywords: technical means, soil cultivation technologies, interaction of working bodies with the soil, soil moisture, soil resistance, energy consumption, physical soil degradation.

Precision Agriculture System Using Big Data Analytics in Predicting Yield by Controlling Plant Disease and Irrigation Scheduling 1. Mr.N.Vasudevan, Research Scholar, Department of Data Science and Business System, School of Computing, Srm Institute of Science and Technology,Ktr

Precision Agriculture System Using Big Data Analytics in Predicting Yield by Controlling Plant Disease and Irrigation Scheduling 1. Mr.N.Vasudevan, Research Scholar, Department of Data Science and Business System, School of Computing, Srm Institute of Science and Technology,Ktr

Experience and problems of implementing digital solutions, elements of precision farming and animal husbandry in agriculture of the Republic of Belarus

The presented review article describes a number of some important developed, implemented and already used software products, information systems, and other digital solutions that are essential for the effective functioning of the agro-industrial complex of the republic. Important global trends in the development of modern agriculture are considered, the benefi ts received by farmers from the use of various digital technologies are discussed, the need to accelerate the current pace of introduction of various elements of precision farming and livestock systems into Belarusian agriculture is noted.

Applications of the internet of things in agriculture

The article examines the methods of implementing Internet of Things technologies in the agricultural sector, suggests the use of artificial intelligence and neural networks in combination with the Internet of Things to improve the results of growing various plants. The advantages of neural networks, which are able to process large arrays of data on the activity of agricultural formations much faster and more efficiently than an experienced specialist, are also considered. However, for this, the primary information for training the network should be prepared in a format that is understandable for it. Obtaining up-to-date and objective information about the condition of plants using the Internet of Things will improve the exchange of information between specialists and expert consultants. Modern agriculture needs to have a high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. IoT is one of the most popular tools of this kind. It is widely used in solving various classification and prediction tasks. For some time IoT also been used in the broadly defined field of agriculture. It can form part of the precision farming and decision support systems. IoT and artificial neural networks can replace the classical methods of modeling, and are one of the main alternatives to classical mathematical models. The spectrum of the applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Adaptive resource capacity management agricultural landscapes of Siberia for efficient cultivation grain crops

In the grain production of Siberia, not everything is provided safely. With a yield potential of 30 - 35 million tons, the actual grain production is 13 - 14 million tons. Cultivation of grain crops is energy intensive, extensive and ecologically unbalanced. The management of the productivity of agrocenoses is carried out by agricultural technologies. This management problem has not been solved even in modern precision farming systems. The purpose of the research is to increase the efficiency of grain crops cultivation by managing the resource potential based on agricultural technologies that take into account spatio-temporal changes in properties and the state of agricultural landscapes in Siberia. Research methods. Structural analysis and synthesis, mathematical modeling of mechanical and technological solutions and the integral indicator of agriculture. Main research results. Intensification is the main way to increase the efficiency of grain production, an analytical expression is proposed for the integral criterion of agriculture - the really possible value of their yield. For the management of agricultural technologies, a 4-level hierarchical concept with mechanical and technological temporary control actions is proposed: the upper level is strategic management. Developed adaptive-mobile field crop rotations, providing 2.72–3.09 MJ growth of the energy coefficient. For the arid regions of Siberia, the Research Institute of Applied Sciences of Khakasia proposed a surface irrigation technology. Provides an increase in labor productivity by 3-5 times, reduces the cost of 1 ton of hay by 3-4 times with an average yield of 5 t / ha; the second level is the management of a separate growing season; a furrow-band sowing was proposed, providing a 20 - 35% increase in yield; the third level is technological control in real time. An automatic flexible technological complex for the main tillage is proposed, which improves the quality of tillage and increases labor productivity by 15 - 20%; the fourth is the level of technological impact on the basis of scientific principles, taking into account the physical, mechanical and technological properties of the treatment object. Conclusions: 1) In the conditions of Siberia with its spatio-temporal changes in the properties and conditions of the soil cover, precise (digital) management of the resource potential of agricultural landscapes is required. 2) To increase the efficiency of grain crops cultivation, four mechanical and technological levels of control have been proposed.

Integration of logistic principles into resource-saving technologies, precision and organic farming

The article presents an analysis of technical means and logistics principles used in the agro-industrial complex of the northern region of Kazakhstan in the implementation of resource-saving technologies, precision and organic farming. The analysis of scientific and technical literature and the production situation shows that the use of individual elements of the precision farming system in various technologies can increase labor productivity by up to 10% and reduce the total costs of cultivating crops by up to 25%, and the use of modern high-performance machine complexes equipped with recommended elements of the precision farming system can increase productivity by 1.7…2.3 times compared to the existing fleet of agricultural machinery. In turn, compliance with the logistics principles of resource provision of agricultural entities at the regional level at the present stage of development is an objective condition for their successful functioning. The allocation of resources should ensure the formation of effective logistics systems that guarantee the refinement of resources to places of consumption in accordance with the interests of all economic entities using resource-saving technologies, precision and organic farming.

ТЕХНИЧЕСКИЕ СРЕДСТВА ДЛЯ ХИМИЧЕСКОЙ ЗАЩИТЫ РАСТЕНИЙ: СОСТОЯНИЕ И ПЕРСПЕКТИВЫ РАЗВИТИЯ

Intensive agricultural production is impossible without the use of plant protection products that contribute to high yields. Agricultural producers highly evaluate the chemical method of plant protection based on the wide use of sprayers as a more efficient and productive one. The analysis of modern sprayers has shown that their design is subject to the dominating trend of increasing productivity, a working width and a tank capacity, improving the quality of work, improving the main components and equipment, using various automation tools to reduce the environmental impact. There are also some long-term trends in the development of autonomous robotic machines and unmanned aerial vehicles, combined into a single intelligent precision farming system. It is noted that the following operating parameters of the sprayer greatly influence the quality indicators of the chemical treatment of plants: the unit speed; the rod installation height and its stability during operation; pressure in the working fluid supply system and nozzle operation parameters (the diameter of droplets formed, the degree of wear and clogging). Environmental parameters also have a significant impact: relative humidity and air temperature, wind speed and the amount of precipitation. To ensure high quality, efficiency, and environmental safety of chemical plant protection, it is necessary to improve the technical means of plant protection through optimizing the main operating parameters, introducing advanced technical solutions aimed at increasing the technological process efficiency; introducing automation and robotization systems of technical means used for plant protection; designing autonomous robotic machines and unmanned aerial vehicles, combined into a single intelligent system of precision farming.

Incremental Knowledge Extraction From IoT-Based System for Anomaly Detection in Vegetation Crops

Precision agriculture systems collect spectral images from satellites, from which vegetation indices (VIs) can be assessed to monitor vegetation and soil condition. It requires a near-daily data acquisition to perform robust crop monitoring and data analysis. Satellites provide a periodic data acquisition that need a further data integration using multiple satellite sources along with camera-equipped drones to achieve an accurate data collection on a selected area. Moreover, VIs are not enough for a proper vegetation evaluation of the monitored areas due to differences among cultivars, the phenological season in which the vegetation is evaluated, the latitude of the areas, etc. This article introduces a system model to detect anomalies regarding the vegetation and soil conditions according to the area phenology and the historical vegetation trends. The system collects spectral images of the regions of interest (ROIs) from satellites and drones, harmonized to calculate VIs and feeds a dataset of near-daily high-resolution integrated images. The harmonic analysis allows phenological data extraction about the ROIs, hence the territorial observation model (TOM) has been extended to represent phenological stages and build knowledge on the ROIs and their phenology that is stored on a triple store. The system selects the VI values, calculated during the learned growing seasons of the ROIs, and classifies them to detect vegetation anomalies affecting those ROIs. The collected knowledge can be used by end-users (e.g., agronomists, experts, etc.) to analyze the anomalies correlated to historical results and vegetation trends.

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a non-destructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia.

The spatial adaptation of farming systems to the heterogeneity of plots

The full exploitation of the resource potential of arable lands was analyzed in the research. The problem of completeness of the use in different farming systems was considered. It was found that at the current stage of development of farming systems, the diversity of soils and lands and, accordingly, their resource potential were most successfully incorporated in adaptive-landscape farming systems and precision farming systems. Undoubtedly, the cost of precision farming systems will decrease in the future due to the cheapening of technical means. But without introducing scientific and methodological justification for accounting of diversity of soils and land plots (as in adaptive-landscape farming systems) and heterogeneity of crops into precision farming systems, increasing the completeness of land resource potential cannot be achieved. Another important direction to improve the full use of the land resource potential is the development of a new scientific direction - Econics, and the development of technologies for leveling the heterogeneity of fields. But these directions are at the very beginning of their development.

Precision and intelligent agricultural decision support system based on big data analysis

ABSTRACT In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects.