Precision Agriculture System Research Articles

A Review on Various Techniques to Transform Traditional Farming to Precision Agriculture

The agricultural sector is of great importance to fulfill food resources need of the country. Precision Agriculture (PA) with Internet of Things and Wireless Sensor Network is a transformation from traditional farming to smart farming. Wireless sensor networks and Internet of Things are considered as drivers to develop system which can change agriculture sector from manual to automatic. Advancement in the technology have pushed the growth of precision agriculture to very large extent despite of several challenges faced in this area. System for precision agriculture relies on hardware components mainly wireless sensors which act as a source for gathering of real time data. Depending upon the real time date retrieved by sensors automation in agriculture is done by adopting decision-based system. With Precision agriculture productivity is optimized by maintaining sustaniability as crop receives what is acutual requirement on the basis of new techniques and software platforms. This review article includes Inernet of Things (IoT), Wireless Sensors, Wireless communication and challenges faced in this area.

Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network

Recently, precision agriculture has gained substantial attention due to the ever-growing world population demands for food and water. Consequently, farmers will need water and arable land to meet this demand. Due to the limited availability of both resources, farmers need a solution that changes the way they operate. Precision irrigation is the solution to deliver bigger, better, and more profitable yields with fewer resources. Several machine learning-based irrigation models have been proposed to use water more efficiently. Due to the limited learning ability of these models, they are not well suited to unpredictable climates. In this context, this paper proposes a deep learning neural network-based Internet of Things (IoT)-enabled intelligent irrigation system for precision agriculture (DLiSA). This is a feedback integrated system that keeps its functionality better in the weather of any region for any period of time. DLiSA utilizes a long short-term memory network (LSTM) to predict the volumetric soil moisture content for one day ahead, irrigation period, and spatial distribution of water required to feed the arable land. It is evident from the simulation results that DLiSA uses water more wisely than state-of-the-art models in the experimental farming area.

Эффективность применения элементов системы точного земледелия в различных категориях хозяйств северного региона Казахстана

The results of substantiating the necessary set of precision farming system components for their effective use in farms of various categories of the northern region of Kazakhstan are presented. It has been established that the use of up-to-date systems of machines equipped with precision farming system components at farms of various categories can increase productivity 1.7-2.3 times in comparison with the existing fleet of agricultural machinery.

Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.

О ПЕРСПЕКТИВАХ ПРЕЦИЗИОННОГО УПРАВЛЕНИЯ ПРОДУКТИВНОСТЬЮ ПШЕНИЦЫ В УСЛОВИЯХ СЕВЕРНОГО КАЗАХСТАНА

More complete realization of the wheat genetic potential will ensure increasing its yield and quality in the conditions of the Republic of Kazakhstan. Innovative agrotechnological approaches, including a precision farming system (PFS) allow the problem to be addressed. Given the large area of Kazakhstan, the incorporation of precision farming systems needs assessment of the state of its vast territories using the Earth remote sensing (ERS) technology. In this paper, for the first time in the conditions of Northern Kazakhstan, the methods of precision management of spring wheat productivity were implemented. The aim of the work was to assess the prospects for precision farming in the conditions of Northern Kazakhstan and to develop an algorithm not only for passive monitoring of the state of crops, but also for active management of the spring wheat yield using ERS. The field trials were performed on dark chestnut soils in the seasons of 2019-2020 (a special precision farming landfill, Baraev Production Center of Grain Farming) with highly productive spring wheat (Triticum vulgare L.) varieties of local selection, the Shortandinskaya 95 and Astana. At the first stage, the main agrochemical indicators of the test sites were characterized using a ground-based mobile complex. Soil fertility parameters were assessed using survey grids with 1, 5, and 25 ha plot grids. The data were used to identify intra-field heterogeneity and calculate fertilizer doses for differentiated application. The second stage involved crop assessment using images from the Sentinel-2 group satellites and photographs form unmanned aerial vehicles (UAV). Plant development during ontogenesis was characterized by normalized difference vegetation index (NDVI), and the yields were finally evaluated using electronic yield maps. At the third stage, the data were processed using the Stat and Microsoft Excel 2010 software package and AGROS-1 software (version 2.09-2.11/1993-2009). The main fertilizers were used at sowing and surface applied. Non-root feeding was also performed. Polymer fertilizers Vitanoll-NP (1 l/ha) and Vitanoll-micro (1Â l/ha) (Agromarket-24 LLC, Russia), a complex chelated fertilizer CHF (2 l/ha) (Agrophysical Research Institute, Russia), and a humic fertilizer Stimullife (0.3 l/ha) (Agrofizprodukt LLC, Russia) were used. It was found that only one agrochemical inhomogeneity of the field was partially leveled by the PFS-based differentiated introduction of fertilizers. Soil fertility, landscape profile and hydrological parameters (soil moisture availability) are shown to dominantly affect the spring wheat yield in the arid conditions of Kazakhstan. The changes in the agricultural landscape conditions due to the slope of the terrain, allows us to trace watercourses that characterize the productive moisture availability in a particular part of the field. On the basis of remote sensing data and coupled ground measurements used to precisely assess and record soil, landscape and soil moisture conditions, an algorithm has been developed to manage the yield formation process in plants. It is shown that the more precise the data analysis, the higher the effect of crop yield management. An increase in yield was approximately 10 % for a 5-ha plot grid and 2 times more for a 1-ha plot grid. The correlation revealed between the wheat yield and NDVI is strong and uneven as depends on the spatial direction, that is, the r values are 0.64-0.99 along the slope transect and 0.62-0.98 broadwise the field. A significant increase in yield was obtained both due to the differentiated application of mineral fertilizers, by 9.5Â % for the 5-ha grid and by 19.2 % the 1-ha grid, and due to timely non-root fertilizing, by 15-22.3 %. In general, our findings indicate the PFS prospects in the management of spring wheat productivity under the conditions of Northern Kazakhstan.

PROSPECTS FOR PRECISION MANAGEMENT OF WHEAT PRODUCTIVITY IN THE CONDITIONS OF NORTHERN KAZAKHSTAN

More complete realization of the wheat genetic potential will ensure increasing its yield and quality in the conditions of the Republic of Kazakhstan. Innovative agrotechnological approaches, including a precision farming system (PFS) allow the problem to be addressed. Given the large area of Kazakhstan, the incorporation of precision farming systems needs assessment of the state of its vast territories using the Earth remote sensing (ERS) technology. In this paper, for the first time in the conditions of Northern Kazakhstan, the methods of precision management of spring wheat productivity were implemented. The aim of the work was to assess the prospects for precision farming in the conditions of Northern Kazakhstan and to develop an algorithm not only for passive monitoring of the state of crops, but also for active management of the spring wheat yield using ERS. The field trials were performed on dark chestnut soils in the seasons of 2019-2020 (a special precision farming landfill, Baraev Production Center of Grain Farming) with highly productive spring wheat (Triticum vulgare L.) varieties of local selection, the Shortandinskaya 95 and Astana. At the first stage, the main agrochemical indicators of the test sites were characterized using a ground-based mobile complex. Soil fertility parameters were assessed using survey grids with 1, 5, and 25 ha plot grids. The data were used to identify intra-field heterogeneity and calculate fertilizer doses for differentiated application. The second stage involved crop assessment using images from the Sentinel-2 group satellites and photographs form unmanned aerial vehicles (UAV). Plant development during ontogenesis was characterized by normalized difference vegetation index (NDVI), and the yields were finally evaluated using electronic yield maps. At the third stage, the data were processed using the Stat and Microsoft Excel 2010 software package and AGROS-1 software (version 2.09-2.11/1993-2009). The main fertilizers were used at sowing and surface applied. Non-root feeding was also performed. Polymer fertilizers Vitanoll-NP (1 l/ha) and Vitanoll-micro (1Â l/ha) (Agromarket-24 LLC, Russia), a complex chelated fertilizer CHF (2 l/ha) (Agrophysical Research Institute, Russia), and a humic fertilizer Stimullife (0.3 l/ha) (Agrofizprodukt LLC, Russia) were used. It was found that only one agrochemical inhomogeneity of the field was partially leveled by the PFS-based differentiated introduction of fertilizers. Soil fertility, landscape profile and hydrological parameters (soil moisture availability) are shown to dominantly affect the spring wheat yield in the arid conditions of Kazakhstan. The changes in the agricultural landscape conditions due to the slope of the terrain, allows us to trace watercourses that characterize the productive moisture availability in a particular part of the field. On the basis of remote sensing data and coupled ground measurements used to precisely assess and record soil, landscape and soil moisture conditions, an algorithm has been developed to manage the yield formation process in plants. It is shown that the more precise the data analysis, the higher the effect of crop yield management. An increase in yield was approximately 10 % for a 5-ha plot grid and 2 times more for a 1-ha plot grid. The correlation revealed between the wheat yield and NDVI is strong and uneven as depends on the spatial direction, that is, the r values are 0.64-0.99 along the slope transect and 0.62-0.98 broadwise the field. A significant increase in yield was obtained both due to the differentiated application of mineral fertilizers, by 9.5Â % for the 5-ha grid and by 19.2 % the 1-ha grid, and due to timely non-root fertilizing, by 15-22.3 %. In general, our findings indicate the PFS prospects in the management of spring wheat productivity under the conditions of Northern Kazakhstan.

Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas.

Deploying wireless sensor networks (WSN) in rural environments such as agricultural fields may present some challenges that affect the communication between the nodes due to the vegetation. These challenges must be addressed when implementing precision agriculture (PA) systems that monitor the fields and estimate irrigation requirements with the gathered data. In this paper, different WSN deployment configurations for a soil monitoring PA system are studied to identify the effects of the rural environment on the signal and to identify the key aspects to consider when designing a PA wireless network. The PA system is described, providing the architecture, the node design, and the algorithm that determines the irrigation requirements. The testbed includes different types of vegetation and on-ground, near-ground, and above-ground ESP32 Wi-Fi node placements. The results of the testbed show high variability in densely vegetated areas. These results are analyzed to determine the theoretical maximum coverage for acceptable signal quality for each of the studied configurations. The best coverage was obtained for the near-ground deployment. Lastly, the aspects of the rural environment and the deployment that affect the signal such as node height, crop type, foliage density, or the form of irrigation are discussed.

Цифровое землеустройство в сфере АПК

The paper analyzes the directions of development of on-farm land management in the context of the formation of innovative approaches to land management, in particular, when introducing a precision farming system in agricultural production in Belarus.

Методологические аспекты оценки эффективности цифровых технологий в точном земледелии

The article presents the results of studies on the development and substantiation of methodological provisions for assessing the effectiveness of digital technologies in precision farming, which are focused on the presence of a cross-cutting and cumulative effect from the introduction of precision farming systems, depending on the stages of development of the digital economy.

Структура сорного компонента и его пространственное распределение в полях зернопропашного севооборота

The article examines the species composition and spatial distribution of weeds, depending on the methods of processing crops of grain-tilled crop rotation. The uneven distribution of weeds over the area of the field requires the development of methods to identify weeds and their spatial location, which will allow the use of differentiated use of herbicides in precision farming systems.

Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r2 = 0.74, mean absolute error; MAE = 14 kg ha−1) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5 ha. Within-field variation in predicted N-uptake was > 30 kg N ha−1 in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7–30 ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r2 = 0.81; MAE = 7 kg ha−1), and can therefore be valuable in a DSS for variable-rate N application.

Cluster-Based Communication Protocol and Architecture for a Wastewater Purification System Intended for Irrigation

The remote location of agricultural fields leads to the difficulty of deploying Precision Agriculture (PA) systems as there is no Internet access in those areas. Therefore, the use of long-range wireless technologies such as LoRa can provide connectivity to rural areas and allow monitoring PA systems remotely. In this paper, a heterogeneous architecture and protocol that allows communication with both WiFi and LoRa, including multiple hops in LoRa are presented. The design is based on a tree topology comprised of electronic devices deployed on different areas of interest for PA systems such as the canals of irrigation water, the fields, and the urban areas that generate wastewater. A set of practical tests with different configurations have been performed to determine the correct operation of the proposed protocol. The results show that the consumed bandwidth for both 433 MHz and 868 MHz frequency bands remained within the limits for the most restrictive LoRa configurations. Therefore, different deployment needs can be addressed with the implementation of this proposal. Furthermore, the use of packet transmission delays of 500 ms at the CH node results in high successful packet delivery rates.

Precision Agriculture: Smart Farming Taken to The Next Level

According to the World Bank, 32 percent of food losses in developing countries can be traced back to conventional farming practices. This is largely due to the lack of attention to detail and the overuse of resources. Conventional farming techniques mainly involve the use of crops throughout an area. This method produces unpredictable and excessive waste production. Before the rise of tech in agriculture, it was highly probable that a farmer would produce bountiful harvests. Since there was no way of knowing what caused crop loss, farmers resorted to asking for help. This condition pushed them into debt. Advancements in satellite imagery and big data analytics have helped the agriculture sector overcome uncertainties. A precision agriculture system is an approach to farming that uses various technologies to improve the efficiency and profitability of the operation. This paper aims to introduce the concept of precision agriculture and its various aspects. It also reviews the various challenges that one has to face in order to successfully implement this field of agriculture.

INTERNET OF THINGS (IOT) FOR SMART PRECISION FARMING AND AGRICULTURAL SYSTEMS PRODUCTIVITY: A REVIEW

The Internet of Things (IoT) is a promising technology which offers effective and reliable solutions for multi-domain modernization. In order to automatically manage and track agricultural farms with minimal human intervention, IoT-based solutions are being created. There are several aspects of innovations involved in the field of IoT in agriculture in the review paper. It describes the key components of precision farming based on IoT. A detailed discussion on network technologies used in IoT-based agriculture, including network architecture and layers, network topologies and protocols, was presented. In addition, the relation of agricultural systems based on IoT with related technologies like cloud computing, storage of big data and analytics was also presented. Furthermore, protection concerns have been illustrated in IoT agriculture. A list of applications based on smart phones and sensors built for various aspects of farm management has also been presented. In addition, the introduction of precise agriculture, which is an alternative to future agriculture, allows for the prediction of supply and demand by means of convergence technology; real-time monitoring and quality maintenance over the life cycle of agricultural products. To increase the productivity of their day-to-day jobs, farmers have already begun to employ some high-tech farming techniques and technologies. Sensors installed in fields, for instance, allow farmers to obtain detailed maps of both the area's topography and resources, as well as variables such as soil acidity and temperature. To predict weather conditions in the coming days and weeks, they can also access climate forecasts. Farmers can remotely track their equipment, crops, and livestock using their smart phones, as well as collect stats on their feeding and producing livestock. They can also use this technology for their crops and livestock to run statistical predictions. And drones have become an invaluable instrument for farmers to sur

Strategies based on IoT for supporting the decision-making in agriculture: a systematic literature mapping

This work implements a systematic mapping study (SMS) of the literature to identify strategies and approaches oriented to support the data-driven decision-making in agriculture using internet-of-thing (IoT) devices. SMS is applied to the Scopus database, with a focus on those data coming from IoT devices, which eventually could be complemented through big data repositories. Seventy-four documents are retained based on the defined filters about the subject. A scoring model is proposed and implemented aim of establishing an order for documents, while a balance between the content precision, the number of citations, and the publishing year. The top ten documents are contrasted, while the rest of the documents are synthesised, highlighting their contributions and opportunities to improve in an ordered way based on the scoring model. The documented main subjects related to real-time decision-making based on IoT devices are represented by precision agriculture, cultivating monitoring, and irrigation management systems.

The use of precision farming technologies in the cultivation of winter wheat in the conditions of the Orel region

The analysis of the use of precision farming systems for the cultivation of winter wheat of the Moskovskaya 39 variety in the climatic and soil conditions of the Orel region of the Russian Federation is carried out. The value of the vegetation index NDVI was used as a reference indicator for determining the amount of macronutrients introduced. The effect of applied fertilizers on winter wheat plants was found to be stimulating for their targeted distribution. The application of fertilizers without taking into account the needs for this site led to uneven plant development, lodging, a decrease in yield and an increase in the cost of production.

Техническое средство мониторинга твердости почвы в системе координатного земледелия

Equipment for determining soil hardness is described, its advantages and disadvantages are analyzed. An electronic hardness tester has been proposed, which makes it possible to compile a map of soil hardness in the field for its use in a precision farming system.

Application of Vegetation Indexes to Assess the Condition of Crops

Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.

Analysis of the use of a combine harvester equipped with a precision farming system in rice growing conditions

On the basis of the Federal State Budget Scientific Institution “Federal Scientific Rice Centre”, studies were conducted to evaluate the effectiveness of the use of the TUCANO 580 combine equipped for harvesting rice with the P420 pick-up. It was established that the combine successfully worked on threshing rice with a shift capacity of about 20 tons per hour. The performance indicators of the combine met the agro-technical requirements (ATT) and amounted to: the total grain loss for the combine 1.99%, including for the thresher 1.55% and for the pick-up 0.44%. Crushing and breaking of bunker grain amounted to 1.2%, which corresponds to the ATT standard - not more than 4.0%. The content of weed impurities was 0,8% (for ATT no more than 5.0%). Along with this, a technique was developed for processing data from a multispectral camera based on the results of surveying by an unmanned aerial vehicle (BVS) and the Sentinel-2A satellite. Based on spectral images, experimental fields, digital maps of the spatial distribution of the normalized relative vegetation index (NDVI) have been created. According to the CLAAS TELEMATICS system, yield maps were constructed. The digital topographic basis of the test ranges was adjusted with the addition of the necessary vector layers based on the results of processing the spectral survey. As a result of the work, a positive assessment of the efficiency of the combine is given, the basis for the creation of a geoinformation base in the information system for the application of precision technologies in agriculture is prepared.

Satellite Remote Sensing Data for Decision Support in Emerging Agricultural Economies: How Satellite Data Can Transform Agricultural Decision Making [Perspectives

This article considers the tremendous potential for satellite remote sensing information delivered via mobile digital applications to improve decision making in emerging agricultural economies. Earth observations have been available for use in weather and other models to support decision making since the late 1970s, with the launch of the advanced very-high-resolution radiometer and the Landsat sensors. Despite early recognition of the potential for satellite remote sensing to transform farm-level decision making, information from satellite data is still not widely used by farmers day to day except in highly mechanized precision agricultural systems that represent a very small minority of farmers globally.