Groom

http://deepblue.lib.umich.edu/bitstream/2027.42/106037/1/me589f13section001project8_report.pd

IoT-based systems for soil nutrients assessment in horticulture

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.info:eu-repo/semantics/publishedVersio

DEVELOPING OF AUTOMATIC FERTILIZER CONTROL SYSTEM IN SOYBEAN PLANT BASED ON INTERNET OF THINGS AND LORA NETWORKS

This research is developing the analog value from the NPK sensor to digital using the YL 38 comparator module to obtain detailed Nitrogen (N), Phosphorus (P), and potassium (K) values according to the NPK sensor datasheet. This system is a network based on the Internet of Things (IoT) and LoRa. The IoT and LoRa features installed on this device, meanwhile the measurement and fertilization data can be monitored easily through an Android application. This research using a frequency of 922.4 Mhz, 125 kHz bandwidth, 10 spreading factors, and a code rate of 5. The Network Quality of Services testing i.e. delay, packet loss, SNR, and RSSI. The QoS was measured at 6 locations. different, 1 location 0 km, 4 locations 1 km, 1 location 2.5 km from BTS LoRa. It was concluded that the parameters used are by the conditions and distances in the data collection. It is proven that all the standards in each parameter are met. In testing the LoRa network it can be concluded that the farther the distance from the LoRa BTS the data transmission quality is getting worse

Soil macronutrient detection based on visible and near-infrared absorption spectroscopy

Precision agriculture using cost-effective soil fertility measurement is important to obtain adequate quality and quantity of crops. Modern agriculture uses soil spectroscopy, which is a fast, cost-effective, environmentally friendly and reusable method. Soil fertility is used in modern agriculture to sustain plant growth and optimize crop yield. However, most existing light sources and computerized photodetection modules in soil spectroscopy are bulky in size, consume high power and expensive such as tungsten-halogen lamps, deuterium lamps and commercial spectrometer. This thesis proposes an improved experimental module based on absorbance spectroscopy to determine the nitrogen (N), phosphorus (P) and potassium (K) in various soil samples which are extracted using colour-developing reagent. The experimental module consisting light-emitting diode (LED) in visible and near-infrared range, and integrated passively quenched silicon photodiode. The optical absorption of various soil samples, including agricultural and non-agricultural soils are experimentally investigated in absorbance mode using an optimal wavelength range of 467 nm until 741 nm. Beer-Lambert Law (BLL) is applied to identify the relationship between the nutrient concentration and the amount of absorbed light. At a wavelength (λ) of 467 nm, N gives a coefficient of determination (R2) between 0.49 and 0.63 for agriculture soil samples. Meanwhile, R2 of agricultural soils for K gives a value from 0.54 to 0.73. At λ = 741nm, P produces R2 in the range of 0.47 to 0.82. Furthermore, research findings using LED and photodiode follow the BLL. BLL states that high concentration has many chemical absorbing species which will lower the transmitted light intensity and give low output voltage. This study has shown that absorbance spectroscopy with proposed LED and photodiode modules are able to distinguish the nutrient concentration in the soil

Particle size and metal distributions in anaerobically digested pig slurry

Particle size distribution and trace element patterns were studied in a full-scale anaerobic digestion plant treating pig slurry. Mass balance was established for major (N, P, K, Ca, Fe, Mg and S) and minor (Al, Cu, Mn and Zn) elements. Most of the elements were conserved through the process but part of the P, Ca, Mg and Mn was deposited as crystals lining the digester. In the dry matter of the slurry, Cu and Zn occurred at between 170 and 2600 mg kg1 due to pig diet supplements. Analyses of particle size distributions in raw and digested slurries showed a general shift in distribution towards larger sizes due to degradation of small and easily degradable particles as well as formation of large microbial filaments. Graded sieving of digested slurry showed metals to be mainly present on 3–25 lm particles. Less than 2% Cu and Zn was removed by passage through a 250 lm rotary screen

Fertilizer Quality Monitoring System in the Supply Chain based on Wireless Sensor Networks

Several farmers are reported to be utilizing substandard fertilizer as a result of supply chain concerns such as inappropriate storage and adulteration by dealers, resulting in soil infertility, low yield, water pollution, and biodiversity loss. The purpose of this research is to demonstrate the construction of a wireless sensor network system capable of collecting and analyzing data from each stage/point in the supply chain, as well as communicating status updates and recommendations to important supply chain partners. The system collected and evaluated data from each stage/point in the supply chain, and it was able to provide status information and advice to the major supply chain players. This enables the detection of changes in the quality of fertilizer prior to its delivery to farmers, allowing for the implementation of appropriate measures. Test results are wirelessly transmitted to the monitoring software's base station server for analysis, display, and storage through a communication module. The host server is comprised of an interpretation program that is used to receive, process, and display data in real-time. Users may obtain information from the base station server through their mobile phones. The remote server of the base station maintains certified fertilizer parameter values for each new batch and the status of reported fertilizer parameter values for each warehouse and provides the report and associated advice to the server and users, respectively. On the one hand, users may use predefined instructions on their mobile phones to seek information about chemical fertilizers and obtain real-time fertilizer nutrient quality metrics. On the other side, the system notifies the server and users of the report and any associated recommendations. The project's results have been positive, and the project's objective is to aid farmers in making better-informed decisions and boosting agricultural yields via the use of technology

A Cost-effective Multispectral Sensor System for Leaf-Level Physiological Traits

With the concern of the global population to reach 9 billion by 2050, ensuring global food security is a prime challenge for the research community. One potential way to tackle this challenge is sustainable intensification; making plant phenotyping a high throughput may go a long way in this respect. Among several other plant phenotyping schemes, leaf-level plant phenotyping needs to be implemented on a large scale using existing technologies. Leaf-level chemical traits, especially macronutrients and water content are important indicators to determine crop’s health. Leaf nitrogen (N) level, is one of the critical macronutrients that carries a lot of worthwhile nutrient information for classifying the plant’s health. Hence, the non-invasive leaf’s N measurement is an innovative technique for monitoring the plant’s health. Several techniques have tried to establish a correlation between the leaf’s chlorophyll content and the N level. However, a recent study showed that the correlation between chlorophyll content and leaf’s N level is profoundly affected by environmental factors. Moreover, it is also mentioned that when the N fertilization is high, chlorophyll becomes saturated. As a result, determining the high levels of N in plants becomes difficult. Moreover, plants need an optimum level of phosphorus (P) for their healthy growth. However, the existing leaf-level P status monitoring methods are expensive, limiting their deployment for the farmers of low resourceful countries. The aim of this thesis is to develop a low-cost, portable, lightweight, multifunctional, and quick-read multispectral sensor system to sense N, P, and water in leaves non-invasively. The proposed system has been developed based on two reflectance-based multispectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). By deploying state of the art machine learning algorithms, the spectroscopic information is modeled and validated to predict that nutrient status. A total of five experiments were conducted including four on the greenhouse-controlled environment and one in the field. Within these five, three experiments were dedicated for N sensing, one for water estimation, and one for P status determination. In the first experiment, spectral data were collected from 87 leaves of canola plants, subjected to varying levels of N fertilization. The second experiment was performed on 1008 leaves from 42 canola cultivars, which were subjected to low and high N levels, used in the field experiment. The K-Nearest Neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the first experiment and 79.2% for the second experiment. In the third and fourth experiments, spectral data were collected from 121 leaves for N and 186 for water experiments respectively; and Rational Quadratic Gaussian Process Regression (GPR) algorithm is applied to correlate the reflectance data with actual N and water content. By performing 5-fold cross-validation, the N estimation shows a coefficient of determination (R^2) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola shows an R^2 of 18.02%, corn of 68.41%, soybean of 46.38%, and wheat of 64.58%. Finally, the fifth experiment was conducted on 267 leaf samples subjected to four levels of P treatments, and KNN exhibits the best accuracy, on the test set, of about 71.2%, 73.5%, and 67.7% for corn, soybean, and wheat, respectively. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf N and P status/content. However, further investigation is needed to improve the water estimation results using the proposed device. Moreover, the utility of the device to estimate other nutrients as well as other crops has great potential for future research

COVER CROPPING: SENSOR-BASED ESTIMATIONS OF BIOMASS YIELD AND NUTRIENT UPTAKE AND ITS IMPACT ON SUGARCANE PRODUCTIVITY

Sugarcane in Louisiana can be harvested for up to three years from one planting. Soil cultivation along sides of established beds is done for weed control and improve fertilizer use efficiency which increases the risk of soil degradation and yield decline. Planting cover crops (CC) is a soil conservation practice and an effective strategy to improve soil health and nutrient recycling. Limited work has been done on remote sensor-based evaluation of the potential nutrient benefits from cover crops and its effect on nutrient cycling on sugarcane systems. This study was conducted to evaluate the effect of two planting methods (broadcast and drilling) and three seeding rates (100%, 50%, and 25% of NRCS recommendation) of a mix of three legumes and two brassicas CC species and a control without CC, on sugarcane yield and quality parameters, and on soil nutrients levels. This study was also used for the acquisition of normalized difference vegetation index (NDVI), collected using GreenSeeker® and multispectral camera (MicaSense® - RedEdge-M) mounted on an unmanned aerial vehicle, to correlate with CC biomass and nutrient uptake. The NDVI readings and CC biomass clippings, using the quadrat frame method, were collected a week before CC termination. Tissue analysis was carried out by C:N dry combustion analyzer and nitric acid digestion-hydrogen peroxide for multi-element analysis. Cane yield was acquired with a chopper harvester and a dump billet wagon. Quality components were obtained by a SpectraCane® automated near infrared (NIR) analyzer for quality parameters. Soil inorganic nitrogen (N) content (NH4+ + NO3-) was quantified using KCl extraction procedure and flow injection analysis. Other soil nutrients content was determined based on Mehlich-3 extraction procedure followed by ICP. A strong positive correlation between the GreenSeeker NDVI (NDVI-GS) and aerial images derived NDVI (NDVI-AI) was obtained with a coefficient of determination (R2) value of 0.63. Adjustment of NDVI with, number of days, cumulative growing degree days, and number of days with positive growing degree days, from planting to sensing increased the R2 values up to 0.76, 0.76 and 0.73, respectively. The NDVI-GS obtain a stronger linear relationship with CC dry biomass and N content than NDVI-AI. Good positive correlations (0.48 \u3e R2 \u3e 0.12) were found between NDVI and some macronutrients (P and K) and micronutrients (Mn and Cu). Overall, there was no significant effect of planting method and seeding rate observed on cane yield and quality parameters. Moreover, there was no statistical difference on CC nutrient removal rate among the treatments (p\u3e0.05). For plant cane, the average cane and sugar yield across sites was 96 Mg ha-1 and 10794 kg ha-1, respectively. Lower yield was attained by the ratoon crops averaging only at 71 Mg ha-1 cane yield and 7197 kg ha-1 sugar yield. Remote sensing is a promising and viable technique to estimate CC biomass and nutrient uptake. Finally, this study corroborates the long-term effect of CC on nutrient management and their effect on cane yield and quality parameters

Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Towards In-situ Based Printed Sensor Systems for Real-Time Soil-Root Nutrient Monitoring and Prediction with Polynomial Regression

This dissertation explores how to increase sensor density in the agricultural framework using low-cost sensors, while also managing major bottlenecks preventing their full commercial adoption for agriculture, accuracy and drift. It also investigated whether low-cost biodegradable printed sensor sheets can result in improved stability, accuracy or drift for use in precision agriculture. In this dissertation, multiple electrode systems were investigated with much of the work focused on printed carbon graphene electrodes (with and without nanoparticles). The sensors were used in two configurations: 1) in varying soil to understand sensor degradation and the effect of environment on sensors, and 2) in plant pod systems to understand growth. It was established that 3) the sensor drift can be controlled and predicted 2) the fabricated low-cost sensors work as well as commercial sensors, and 3) these sensors were then successfully validated in the pod platform. A standardized testing system was developed to investigate soil physicochemical effects on the modified nutrient sensors through a series of controlled experiments. The construct was theoretically modeled and the sensor data was matched to the models. Supervised machine learning algorithms were used to predict sensor responses. Further models produced actionable insight which allowed us to identify a) the minimal amounts of irrigation required and b) optimal time after applying irrigation or rainfall event before achieving accurate sensor readings, both with respect to sensor depth placement within the soil matrix. The pore-scale behavior of solute transport through different depths within the sandy soil matrix was further simulated using COMSOL Multi-physics. This work leads to promising disposable printed systems for precision agriculture