Concept Of Precision Agriculture Research Articles

Designing and Static Performance Testing of Auger Type Fertilizer Metering Device with PID Control

The rapid development of technology has brought a positive impact on the agricultural sector, primarily through the concept of precision agriculture. Precision fertilization is critical to improving agricultural quality and productivity by adapting the right actions to the right place at the right Time according to land variability. One approach that is considered successful is fertilization based on nutrient adequacy levels, which can provide optimal fertilization recommendations. The purpose of this study is to obtain an auger-type variable rate mating device that can control the dose of granular fertilizer. The results showed that the variable rate metering device auger type that had been tested could work quite well in controlling the dose of granular fertilizer. This can be seen from the static calibration of the auger-type metering device producing a linear correlation of each motor with a correlation equation y = 0.0041x – 0.025 with a coefficient of determination R2 = 0.9543 and the results of testing stair step response and step response both without fertilizer and with fertilizer show that the system can follow the set point commands given.

Recommendation System to Precision Agriculture Using Machine Learning Algorithm

The Indian economy heavily relies on agriculture, a vital source of livelihood for millions of farmers and a significant contributor to the GDP. However, farmers often face challenges in selecting the most suitable crops for their specific soil and environmental conditions. This decision profoundly influences crop productivity and the overall agricultural industry. Addressing this issue is crucial, and crop prediction plays a pivotal role in the emerging concept of precision agriculture. Precision agriculture seeks to streamline the crop selection process by leveraging machine learning techniques. The model takes into account various factors such as soil type, rainfall patterns, temperature, groundwater levels, availability of pesticides and fertilizers, and the current season. These elements are intricately analyzed to create a robust recommender system. The ultimate objective is to expedite and enhance the crop selection procedure for farmers. By incorporating data-driven insights, this precision agriculture approach aims to empower farmers with informed decisions tailored to their unique agricultural landscapes. The recommender system acts as a valuable tool, offering guidance on the most suitable crops for optimal yield and sustainability. This initiative not only addresses the challenges faced by Indian farmers but also contributes to the overall efficiency and resilience of the agricultural sector. In summary, the integration of machine learning and data analytics in crop prediction for precision agriculture holds the potential to revolutionize decision-making processes and positively impact crop productivity in the Indian agricultural landscape.

Humic Acid for Soil Quality Improvement with Application of Scan Automation on Seedless Watermelon Cultivation

Purpose: Sustainable agricultural intensification needs to be well developed. Therefore, food and agricultural production activities can be continuously produced without damaging the ecosystems and environment. One that can be used is a plant biostimulant based on humus. This research was conducted to examine the effect of humic acid as a soil biostimulant and the impact of its use on the yield of seedless watermelon cultivated on marginal land. This study involve IoT technology through monitoring soil quality which is carried out based on the concept of precision agriculture using the farm management system (FMS) Agrooscan tool. Methods: The study used humic acid factor for five treatments (0, 2, 4, 6, and 8 g/L). Observations included soil conditions monitored with Agrooscan instruments such as soil pH, nutrients (N, P, K), as well as measuring plant height, fruit weight, fruit volume and total sugar content (Brix). Results: The results showed that application of humic acid increased the nutrient content in the soil and significantly affected soil pH and seedless watermelon yields. The 8 g/L treatment gave the highest soil N and K values, and was significantly different from no treatment (control). Conclusions: Thus, exogenous use of humic acid as a soil nutrient supplement can be used to help increase marginal land use, including in the development of seedless watermelon cultivation. The use of Agrooscan as a tool to scan soil conditions plays a very important role in maintaining the quality of nutrients so that the growth of watermelon cultivation can run better.

Remote Sensing Related Tools and their Spectral Indices Applications for Crop Management in Precision Agriculture


 World population increased rapidly has increased food demands for human and fulfill the food requirements with limited available resources of the planet is a big challenge for Agriculture. Farmers will need to increase the food production, either the increasing the agricultural land or enhancing crop productivity in agriculture by using different crop management practices and adopting new methods like precision farming. Concept of precision agriculture that involves integrating new technologies and field data to accomplish the right input at the right time in the right place. However, the agricultural sector is yet to adopt remote sensing technologies fully due to lack of knowledge on their sufficiency, appropriateness and techno-economic feasibilities. This study based on the research literature that focused on the application of remote sensing tools in precision agriculture on different aspect of crop management from field preparation to crop harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. Remote sensing tools and spectral vegetation index (normalized difference vegetation index & others) to support crop management and decisions making at different crop growth stages of crop production in precision agriculture, ranging from field preparation, weather, insect pest management, biotic & abiotic stress management and in-season crop health monitoring to harvest.

Application of elements of the information and analytical system of crop management to ensure resource-saving production of flax seeds in the Tver region

Relevance. The concept of precision agriculture call for the use of physical and mathematical, technical and software tools for obtaining and processing information about agroecosystems, as well as for the implementation of agricultural practices directly in the field. To date, there are quite a few software products that can control the production of crops, in particular, the management of differentiated fertilization and compliance with elements of cultivation technology. The purpose of our research conducted over two years was to develop and test in real field conditions algorithms for the functioning of the module of the information and analytical system of crop management for specialized crop rotations involving flax.Methods. The objects of research were the elements of the information and analytical management system for resource-saving production of crop production "Vash urozhaj" (IASUR) and flax-long-lived variety Tonus (selection of the FSBI FNC LC). The experiment used an electronic field map in kml file format containing the coordinates of points, quantitative characteristics of indicators of the content of nutrients and chemical elements in elementary areas within the surveyed spatial object, a technological map of the cultivation of flax, developed specifically for this variety, task maps for the application of solid and liquid fertilizers in .csv file format.Results. Developed and formed in the information and analytical system "Vash urozhaj" electronic technological map of the cultivation of flax-long-legged variety Tonus, containing a list of technological operations, timing and quality characteristics of their implementation, including a possible set of machinery and equipment. The possibility has been tested and the effectiveness of the module for obtaining climatically secured flax yield has been determined — the planned estimated yield for this flax variety at the pilot site is 4.3 c/ha. When cultivated according to the technology adopted in the Federal Scientific Center of Bast Cultures, introduced into the agroplatform, a yield of 4.0 c/ha was obtained. The yield deviation by 6.98% relative to the calculated one is due to the weather features of the growing season of 2021.

Development of an algorithm for spectral analysis of images from UAVs to assess the quality of crop sprouts

Within the framework of the concept of precision agriculture, a timely assessment of crop seedlings is necessary for making managerial decisions. Today, in most farms, this assessment is carried out on the basis of a limited number of field observations. In this paper, it is proposed to use unmanned aerial vehicles (UAV) as a data source for assessing seedlings, with the help of which a one-time survey of significant territories is provided. The proposed technology of automated image processing from UAVs allows you to quickly obtain not only a qualitative, but also a quantitative assessment of seedlings. The article considers an algorithm for spectral analysis of images from UAVs to assess the quality of crop sprouts.

Terahertz-Based Joint Communication and Sensing for Precision Agriculture: A 6G Use-Case

By 2050, experts estimate that the agricultural produce must increase by 60%–70% to meet the needs of the ever increasing population of the world. To this aim, the concept of precision agriculture or smart farming has recently been coined. The idea of precision agriculture is well represented as a smart management system, having the ability to monitor, observe, sense, measure and control the health and water contents in plants at nano-scale and crops at macro-scale. The goal is to maximise the production while preserving the vital resources. The combination of terahertz (THz) based sensing technology to estimate plant health at a cellular level, and wireless sensor networks deployed within crops to monitor different variables while making intelligent decisions is far reaching. The integration and operation of such a macro-nano-sensor system requires a sustainable communication infrastructure that considers the demands of remote and agile agricultural environments. In this paper, an integrated sensing and communication system for plant health monitoring that utilises THz signals, is presented as a 6G use case. The joint architecture is outlined and various challenges including energy harvesting, practical implementation among others, followed by recommendations for future research are presented.

Futuristic IoT based Smart Precision Agriculture: Brief Analysis

Agriculture is considered as the backbone of any nation across the globe. With the advent of modern technologies, smart tools and techniques are used in the agriculture/farming to build on the quantity as well as quality of the agriculture production to feed the basic necessity of the humans. Smart technology such as Internet of Things play a vital role in monitoring and analyzing various environmental parameters such as water level, humidity, soil moisture, air quality, UV level, rain etc. which are highly essential to ensure the fruitful yield of any nutritious crops. In this research article, precision agriculture concepts are investigated widely with the focus of improving the productivity level and also the effective utilization of resources with the minimal cost while compared with the conventional methodologies.

Using a single-board computer as a low-cost instrument for SPAD value estimation through colour images and chlorophyll-related spectral indices

The leaf chlorophyll content is a major indicator of plant stress. Therefore, it is often used for the evaluation of crop status to adjust agricultural management to ensure high quality yield while concurrently applying water and agrochemicals in a sustainable manner. Since laboratory procedures for their assessment are time-consuming and destructive, nondestructive methods have been developed recently based on known vegetation spectral response characteristics. In addition to various vegetation indices derived from remotely sensed data, hand-held sensors such as SPAD-502 are currently widely used for in-field sampling to gain precise information for decision-making in terms of best-fitting agricultural management. However, the costs of such commercial devices can be limiting for farmers. The low-cost alternatives that have been developed recently exploit widely accessible digital cameras with sensors sensitive to the visible region of the electromagnetic spectrum. Digital numbers extracted from colour images in RGB channels serve as the input for broadband “chlorophyll index” calculations. Major constraints regarding digital cameras are, however, the natural light illuminance and the necessity of data postprocessing. In the framework of this study, a novel technological solution was developed to address these issues. A Raspberry Pi single-board computer together with a Pi Camera and a simple LED incorporated in a 3D print case created a prototype called Rasp2SPAD, which was programmed to acquire and analyse a colour image. The prototype and its setup were further tested on the experimental plant material of the winter rapeseed. A set of 22 chlorophyll-related parameters across various colour representation models were generated, from which an SPAD value was modelled using i) a simple linear model, ii) a generalized linear model, and iii) an artificial neural network. The blue (Cb) and red (Cr) chroma components of the YUV colour space were found to be most suitable for SPAD value modelling. Calibration equations were determined, and the results reached relatively high accuracy (mean absolute deviance 1.85 and R-squared 0.81 for simple linear model) while keeping the costs significantly low compared to the most commonly used commercial sensor. In this way, a simple and cheap methodology was introduced to bring the results of research closer to practice, which should help first spread the precision agriculture concept to a wider audience and second allow them to utilize with it.

A Versatile, Stand-Alone, In-Field Sensor Node for Implementation in Precision Agriculture

The concept of Precision Agriculture has been conceived several decades ago however, it has never been fully implemented due to several technological constraints, such as the high cost of automated systems. In this paper, a versatile, low-cost and stand-alone sensing node with unique capabilities is presented, suitable for direct soil quality monitoring. The node is equipped with a potentiostat to run electrochemical measurements as well as a circuit for potentiometric measurements, a circuit to accommodate a wide variety of resistance thermometers and a circuit to detect impedance at 1 kHz. All analog circuits feature accuracies of less than 5% with linearity of more than R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.999. The system is battery-powered and can recharge through a solar panel or a power supply. Bluetooth, WiFi and LoRa communication technologies are supported making the system IoT compatible whilst maintaining a low cost. The designed instrumentation is versatile enough so that it can accommodate other sensors operating under the same principles with different sensitivities, as well as commercial sensors communicating through SPI and I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C protocols. All the modifications to the system can be done through software during sensor calibration.

KARAKTERISTIK, POTENSI GENERASI MILENIAL DAN PERSPEKTIF PENGEMBANGAN PERTANIAN PRESISI DI INDONESIA

&lt;p&gt;Precision agriculture requires appropriate characters of human resources to implement it. It is an integrated agricultural system based on information and production to increase business efficiency, productivity and profitability. The concept of precision agriculture, as one of the latest agricultural technology packages, was born along with the emergence of the millennial generation, namely those born between 1980 and 2000.This paper discusses the character of precision agriculture and necessity to apply it and its link to the millennial generation in terms of their character suitability and capacity. Application of precision agriculture requires the millennial generation’s ability to create, engineer and operate modern agricultural systems based on this new technology. Applying precision agriculture in Indonesia deals with various characteristics of the millennial generation due to different regional and socio-economic conditions. The government should provide infrastructure and conduct millennial farmers training to achieve social, economic, and environmental benefits of precision agriculture implementation.&lt;/p&gt;

Three-dimensional (3D) reconstruction for non-destructive plant growth observation system using close-range photogrammetry method

Agriculture in Indonesia has recently experienced challenges, especially in terms of meeting market demand that tends to increase. Precision agriculture is an effort to increase productivity through optimizing agricultural production processes by minimizing inputs, maximizing output, and reducing the impact on the environment through the use of information and communication technology. Implementation of the concept of precision agriculture is in observing plant growth and development to estimates of plant volume and mass. So far, conventional plant measurements by direct measurement can pose destructive risks to plants. Therefore, a non-destructive system for observing and measuring plants is needed. The purpose of this study was to develop a plant growth observation system based on three-dimensional modelling with non-destructive measurements using the Close-Range Photogrammetry (CRP) method. This system has a 360 orbital scanner component that consists of a frame, camera arm, NEMA 17 stepper motor, Logitech C270 web-camera, Raspberry Pi 3 B +, Adafruit motor driver, and LED strip lights. The stage of image processing using 3DF Zephyr Pro software for generating the 3D sparse point cloud and cropping the unused point. For the validation purposes and its functionality for modelling and estimating volumetric objects, a plant model with three sizes (small, medium, and large) variations were used. As the result, the developed system could observe and model the plant shape in a three-dimensional manner resemble the actual plant model.

Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

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.

Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt

The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR-SWIR spectroscopy, partial least-squares regression (PLSR), Ordinary Kriging (OK), and Landsat-8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El-Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR-SWIR spectroscopy showed high performance (where R2 was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat-8 OLI images (correlation R2 values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El-Garawla are characterized by low-to-moderate fertility, while about 16% of the area is characterized by high soil fertility.

Development of Precision Agriculture Models for Medium and Small-Scale Agriculture in Indonesia

The purpose of this research is to build a model of precision agriculture to increase the productivity of medium and small-scale agriculture in Indonesia. This research used a descriptive method by conducting a literature study on the concept of precision agriculture and analysis of data related to agriculture in Indonesia. Extreme weather changes in Indonesia have an impact on the decline in agricultural production. While an understanding of environmental conditions is the primary key to agriculture, this research explained what data is needed in precision agriculture, how to obtain and process data, also explained how to visualize information through geographic information systems. This research also conducted an assessment of the built model. The assessment process is implemented by analysing the suitability of the model based on farmers’ needs. The results of this study concluded that the model can be developed in the implementation phase. This research is expected to provide a clear explanation of the system requirements and also can reduce risk at the system development and implementation stage.

Implementation of crop growth monitoring system based on depth perception using stereo camera in plant factory

The plant factory is extensive cultivation to produce a high quality of vegetables under a controllable environment. The concept of Precision Agriculture (PA) was introduced to improve the plant factory production by the implementation of a crop growth monitoring system. Crop growth can be estimated by monitoring of crop height and canopy foliage by the use of computer vision technology. In our previous study, we have introduced a plant height monitoring system based on depth perception using a stereo camera. However, the validity of the various type of leave is necessary to be tested. The objective of this study was to implement the crop growth monitoring system to monitor plant development with various type of leave for system validation and evaluation. The crop growth monitoring system composed of a stereo camera implementing the depth perception for estimating the distance from camera to highest point in the crop. The implementation of the system with various types of leaves and characteristics has been conducted for (a) Samhon, (b) Lettuce, and (c) Pagoda. The developed crop growth monitoring system could perform the time series estimation of crop height with a maximum error of RMSE 0.875 cm on Pagoda, and MAPE of 5.56% on Lettuce. The system demonstrates better estimation on Samhong with minimum error RMSE of 0.408cm and 2.27 % of MAPE. Overall validation for the estimated height vs actual measurement indicates that the coefficient of determination higher than 0.7 means that it has substantial features for estimating the plant height.

Engineering Biomimetic Calcium Phosphate Nanoparticles: A Green Synthesis of Slow-Release Multinutrient (NPK) Nanofertilizers.

Biomimetic calcium phosphate nanoparticles (CaP) have been actively used in biomedicine, due to their high biodegradability and biocompatibility. However, much less progress has been made regarding their application in precision agriculture, i.e., for the controlled delivery of active species to plants. Herein, we report a straightforward and green synthetic method to dope CaP with potassium (K) and nitrogen (N, as nitrate and urea). By modulating the synthetic conditions in terms of maturation time (at 37 °C) and doping, we prepared K- and N-doped nanoparticles in the form of either amorphous calcium phosphate (ACP) or nanocrystalline apatite (Ap) and studied the impact of the dopants on the ACP-to-Ap transformation pathways. Importantly, we found out that ACP, isolated at low maturation times, incorporates nitrogen (in the form of nitrate and urea) to a larger extent than Ap (2.6 vs 1.1 wt %, respectively). Multinutrient nanofertilizers (so-called nanoU-NPK) with the following composition (wt %) were obtained: Ca (23.3), P (10.1), K (1.5), NO3 (2.9), and urea (4.8). The nanoU-NPK provides a slow and gradual release of the most important plant macronutrients (NPK), with N in two chemical forms, and different kinetics. The concentration of nutrients supplied by 10 g L-1 of nanoU-NPK to the media after 1 week (in mg L-1) was Ca (27.0), P (6.2), K (41.0), NO3 (134.0), and urea (315.0). Preliminary tests on durum wheat have shown that the application of nanoU-NPK allows reducing the amount of nitrogen supplied to the plants by 40% with respect to a conventional treatment, without affecting the final kernel weight per plant. The application of these slow-release NPK nanofertilizers is a promising strategy toward enhancing the efficiency of the fertilization, complying with the concept of precision agriculture.

May smart technologies reduce the environmental impact of nitrogen fertilization? A case study for paddy rice.

Precision agriculture is increasingly considered as a powerful solution to mitigate the environmental impact of farming systems. This is because of its ability to use multi-source information in decision support systems to increase the efficiency of farm management. Among the agronomic practices for which precision agriculture concepts were applied in research and operational contexts, variable rate (VR) nitrogen fertilization plays a key role. A promising approach to make quantitative, spatially distributed diagnoses to support VR N fertilization is based on the combined use of remote sensing information and few smart scouting-driven ground estimates to derive maps of nitrogen nutrition index (NNI). In this study, a new smart app for field NNI estimates (PocketNNI) was developed, which can be integrated with remote sensing data. The environmental impact of using PocketNNI and Sentinel 2 products to drive fertilization was evaluated using the Life Cycle Assessment approach and a case study on rice in northern Italy. In particular, the environmental performances of rice fertilized according to VR information derived from the integration of PocketNNI and satellite data was compared with a treatment based on uniform N application. Primary data regarding the cultivation practices and the achieved yields were collected during field tests. Results showed that VR fertilization allowed reducing the environmental impact by 11.0% to 13.6% as compared to uniform N application. For Climate Change, the impact is reduced from 937.3 to 832.7kg CO2 eq/t of paddy rice. The highest environmental benefits - mainly due to an improved ratio between grain yield and N fertilizers - were achieved in terms of energy consumption for fertilizer production and of emission of N compounds. Although further validation is needed, these preliminary results are promising and provide a first quantitative indication of the environmental benefits that can be achieved when digital technologies are used to support N fertilization.

ROLNICTWO PRECYZYJNE – KONCEPCJE I PERSPEKTYWY

The development of technologies in the 20th century led to evolution of precision agriculture concept. Nowadays, precision farming is usually associated with the use of GPS and satellite navigation, GIS, unmanned airplanes and drones, variable rate of application, as well as complex and sophisticated computer systems and software. On the other hand, the main question is related to the profitability and efficiency of these technologies and the opportunities for their adoption. The main purpose of the research is to investigate the most popular concepts of precision farming and to analyze the technical and economic efficiency of different technologies based on literature review. The results indicate that the adoption of precision farming technologies is closely related to the farmer’s perception of and needs for institutional support. The promotion of precision farming under the Common Agricultural Policy is necessary in order to overcome the number of economic and environmental challenges and ensure sustainable development and green growth.