Search Results (40)

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

Pattern recognition in agricultural soils using interdigitated microwave sensors combined with principal component analysis offers a novel approach to soil characterization. In this study, soil samples were collected at the “El Potrillo” ranch, Chihuahua, Mexico, following extraction and preparation protocols. The results of the PCA of the soils revealed that the first two principal components (PC1 and PC2) explain 99.99% of the variability, with the first principal component accounting for 99.73% of the total variability, allowing for effective discrimination of the samples. A high correlation was observed between the behavior patterns of the deeper samples in the soil and the reference solutions with a lower glyphosate concentration. On the other hand, the samples from the soil surface showed greater similarity to deionized and distilled water. Furthermore, when evaluating interdigitated sensor configurations, it was determined that the 3F sensor is redundant and can therefore be excluded. These findings highlight the effectiveness of the combined use of microwave sensors and PCA to identify patterns in agricultural soils. Full article

This article presents a systematic literature review on Information and Communication Technologies (ICTs) applied to precision agriculture, focusing on their relevance to Colombia. It identifies key technical and administrative needs for digital transformation in the sector and proposes a conceptual roadmap for implementation. Findings highlight the potential of early warning systems (EWSs), the Internet of Things (IoT), and artificial intelligence (AI) to improve productivity, sustainability, and climate resilience. The study outlines current adoption barriers and proposes future empirical validation through field experiments. Full article

Durum wheat (Triticum durum Desf.), among the herbaceous crops, is one of the most extensively grown in the Mediterranean area due to its fundamental role in supporting typical food productions like bread, pasta, and couscous. Among the environmental and technical aspects, nitrogen (N) fertilization is crucial to shaping plant development and that of kernels by also affecting their protein concentration. Today, new techniques for monitoring fields using uncrewed aerial vehicles (UAVs) can detect crop multispectral (MS) responses, while advanced machine learning (ML) models can enable accurate predictions. However, to date, there is still little research related to the prediction of the N nutritional status and its effects on the productivity of durum wheat grown in the Mediterranean environment through the application of these techniques. The present research aimed to monitor the MS responses of two different wheat varieties, one ancient (Timilia) and one modern (Ciclope), grown under three different N fertilization regimens (0, 60, and 120 kg N ha⁻¹), and to estimate their quantitative and qualitative production (i.e., grain yield and protein concentration) through the Pearson’s correlations and five different ML approaches. The results showed the difficulty of obtaining good predictive results with Pearson’s correlation for both varieties of data merged together and for the Timilia variety. In contrast, for Ciclope, several vegetation indices (VIs) (i.e., CVI, GNDRE, and SR_RE) performed well (r-value > 0.7) in estimating both productive parameters. The implementation of ML approaches, particularly random forest (RF) regression, neural network (NN), and support vector machine (SVM), overcame the limitations of correlation in estimating the grain yield (R² > 0.6, RMSE = 0.56 t ha⁻¹, MAE = 0.43 t ha⁻¹) and protein (R² > 0.7, RMSE = 1.2%, MAE 0.47%) in Timilia, whereas for Ciclope, the RF approach outperformed the other predictive methods (R² = 0.79, RMSE = 0.56 t ha⁻¹, MAE = 0.44 t ha⁻¹). Full article

Agriculture, an essential driver of economic expansion, is faced by the issue of sustaining an increasing global population in the context of climatic uncertainty and limited resources. As a result, “Smart Farming”, which uses cutting-edge artificial intelligence (AI) to support autonomous decision-making, has become more popular. This article explores how the Internet of Things (IoT), AI, machine learning (ML), remote sensing, and variable-rate technology (VRT) work together to transform agriculture. Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. Furthermore, the precision with which VRT applies water, pesticides, and fertilizers optimizes resource utilization, enhancing sustainability and efficiency. To effectively meet the world’s food demands, this study forecasts a sustainable agricultural future that combines AI-driven approaches with conventional methods. Full article

The increasing atmospheric CO₂ concentration due to anthropogenic activities has led to the development of low-cost, portable, and user-friendly sensing technologies. Non-Dispersive Infrared (NDIR) sensors offer reliable CO₂ detection with high sensitivity, which makes them ideal for citizen scientists. In this context, we designed two low-cost CO₂ monitoring systems: an automatic opening chamber with a lid and a portable device using NDIR sensors. These monitoring systems were calibrated (R² = 0.99) with known CO₂ concentrations. Besides its reliability and accuracy, the Automated CO₂ Monitoring System costs approximately USD 220.77 and portable CO₂ device costs USD 151.43, which makes them suitable for citizen scientists. Due to CO₂ gas monitoring system’s simplicity, structure, and operation, non-expert users can use and actively participate in environmental monitoring data collection. This promotes public engagement in climate and air quality monitoring and enables citizen scientists to have reliable data for CO₂ monitoring and environmental awareness. Full article

Precision Farming (PF) in vineyards represents an innovative approach to vine cultivation that leverages the advantages of the latest technologies to optimize resource use and improve overall field management. This study investigates the application of PF techniques in a vineyard, focusing on sensor-based decision-making for autonomous driving. The goal of this research is to define a repeatable methodology for virtual testing of autonomous driving operations in a vineyard, considering realistic scenarios, efficient control architectures, and reliable sensors. The simulation scenario was created to replicate the conditions of a real vineyard, including elevation, banking profiles, and vine positioning. This provides a safe environment for training operators and testing tools such as sensors, algorithms, or controllers. This study also proposes an efficient control scheme, implemented as a state machine, to autonomously drive the tractor during two distinct phases of the navigation process: between rows and out of the field. The implementation demonstrates improvements in trajectory-following precision while reducing the intervention required by the farmer. The proposed system was extensively tested in a virtual environment, with a particular focus on evaluating the effects of micro and macro terrain irregularities on the results. A key feature of the control framework is its ability to achieve adequate accuracy while minimizing the number of sensors used, relying on a configuration of a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU) as a cost-effective solution. This minimal-sensor approach, which includes a state machine designed to seamlessly transition between in-field and out-of-field operations, balances performance and cost efficiency. The system was validated through a wide range of simulations, highlighting its robustness and adaptability to various terrain conditions. The main contributions of this work include the high fidelity of the simulation scenario, the efficient integration of the control algorithm and sensors for the two navigation phases, and the detailed analysis of terrain conditions. Together, these elements form a robust framework for testing autonomous tractor operations in vineyards. Full article

Hydroponics, a soilless cultivation method, relies on precise nutrient management to optimize plant growth. This study provides a systematic scoping review of automated hydroponic nutrient dosing systems, focusing on potential of hydrogen (pH) and electrical conductivity (EC) dosing frameworks. Following preferred reporting items for systematic reviews and meta-analyses extension for systematic scoping reviews (PRISMA-ScR) guidelines, 3222 studies were retrieved and screened, with 89 meeting inclusion criteria for analysis. The review aimed to identify current research trends, dosing frameworks, critical variables, and research gaps. Results reveal a steady rise in publications from 2015 (n = 4) to 2022 (n = 18). Feedback loop frameworks and predictive analytics are equally represented (n = 45 each). Critical variables include pH (n = 70), EC (n = 36), total dissolved solids (TDS) (n = 27), nutrient solution volume (NSV) (n = 42), and nutrient solution temperature (NST) (n = 28). The study highlights the need for robust frameworks incorporating advanced dosing frameworks and simultaneous dosing strategies to enhance dosing speed, accuracy, and robustness. A novel framework is proposed to address these gaps by integrating predictive analytics using multiple regression models. This framework aims to improve the dosing speed, accuracy, and robustness of automated hydroponic nutrient dosing systems. The findings underscore the importance of further research into adaptive frameworks to meet the growing demand for precision hydroponic systems. Full article

The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m² to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification. Full article

Closed hydroponics is an environmentally friendly and economical method for growing crops by circulating a nutrient solution while measuring and supplementing various ions contained in the solution. However, conventional monitoring systems in hydroponics do not measure individual ions in the nutrient solution; instead, they predict the total ion content from the pH and electrical conductivity (EC). This method cannot be used to supplement individual ions and adjusts the concentration of the circulating nutrient solution by diluting or supplying a premixed nutrient solution. A more advanced system should be able to identify the concentration of each ion in the nutrient solution and supplement any deficient ions, thus requiring individual ion monitoring systems. Therefore, we first investigated the nitrate, ammonium, phosphate, and potassium (NPK) ion concentration and pH range commonly used for nutrient solutions. Subsequently, we discuss the latest research trends in electrochemical and optical sensors for measuring NPK ions. We then compare the conventional monitoring system (pH and EC-based) and advanced monitoring systems (individual ion sensors) and discuss the respective research trends. In conclusion, we present the hurdles that researchers must overcome in developing agricultural ion sensors for advanced monitoring systems and propose the minimum specifications for agricultural NPK ion sensors. Full article

A protocol for detecting the potential occurrence of spontaneous combustion (SC) in stored cottonseeds and peanuts using a micro-incubator is described. The protocol indicates how to quantify CO₂ production rates and final CO₂ levels in wet versus dry cottonseed and peanut samples, which can provide crucial data for the early detection of SC risk in storage facilities. The experimental design utilizes a micro-incubator to simulate conditions found in large bulk crop storage. Parameters monitored include CO₂ concentration, temperature, and relative humidity. The protocol includes preparation methods, experimental procedures for both control (dry) and wet seed tests, and test termination criteria that allow for safe experimentation of likely pathogenic fungi. The protocol has three replicates for wet and dry conditions. The protocol is intended to facilitate future experimental studies and ultimately contribute to the development of a consistently reliable early warning fire detection system for SC in cottonseed and peanut warehouse facilities. A consistently reliable fire detection system would address a critical need in the cotton and peanut industry for improved fire risk management and insurability of storage facilities. Full article

Soil monitoring is essential for promoting sustainability in agriculture, as it helps prevent degradation and optimize the use of natural resources. The introduction of innovative technologies, such as low-cost sensors and intelligent systems, enables the acquisition of real-time data on soil health, increasing productivity and product quality while reducing waste and environmental impact. This study examines various agricultural monitoring technologies, focusing on soil moisture sensors and nutrient detection, along with examples of IoT-based systems. The main characteristics of these technologies are analyzed, providing an overview of their effectiveness and the key differences among various tools for optimizing agricultural management. The aim of the review is to support an informed choice of the most appropriate sensors and technologies, thus contributing to the promotion of sustainable agricultural practices. Full article

Climate change has intensified droughts, severely impacting crops like oats and highlighting the need for effective adaptation strategies. In this context, the implementation of IoT-based climate control systems in greenhouses emerges as a promising solution for optimizing microclimates. These systems allow for the precise monitoring and adjustment of critical variables such as temperature, humidity, vapor pressure deficit (VPD), and photosynthetically active radiation (PAR), ensuring optimal conditions for crop growth. During the experiment, the average daytime temperature was 22.6 °C and the nighttime temperature was 15.7 °C. The average relative humidity was 60%, with a VPD of 0.46 kPa during the day and 1.26 kPa at night, while the PAR reached an average of 267 μmol m⁻² s⁻¹. Additionally, the use of high-throughput gravimetric phenotyping platforms enabled precise data collection on the plant–soil–atmosphere relationship, providing exhaustive control over water balance and irrigation. This facilitated the evaluation of the physiological response of plants to abiotic stress. Inoculation with microbial consortia (PGPB) was used as a tool to mitigate water stress. In this 69-day study, irrigation was suspended in specific treatments to simulate drought, and it was observed that inoculated plants maintained chlorophyll b and carotenoid levels akin to those of irrigated plants, indicating greater tolerance to water deficit. These plants also exhibited greater efficiency in dissipating light energy and rapid recovery after rehydration. The results underscore the potential of combining IoT monitoring technologies, advanced phenotyping platforms, and microbial consortia to enhance crop resilience to climate change. Full article

With recent advances in spray technology and rising interest in site-specific applications, it is imperative to assess the performance of the latest application technologies to ensure effective pesticide applications. Thus, a study was conducted to compare and evaluate the performance of two different flow control systems [rate controller (RC) and pulse width modulation (PWM)] on an agricultural sprayer while simulating different site-specific application scenarios. A custom data acquisition and logging system was developed to record the real-time nozzle flow and pressure across the sprayer boom. The first experiment measured the response time to achieve different target application rates in single-rate site-specific (On/Off) states at varying simulated ground speeds. The second experiment examined the response time for rate transitions in variable-rate application scenarios among different selected target rates at varying simulated ground speeds. Across all the application scenarios, the PWM system consistently outperformed the RC system in terms of response time and rate stabilization. Specifically, the PWM system exhibited significantly lower mean rate stabilization times compared to the RC system during single-rate application states. Similarly, in the variable-rate application states—where the rate transitions were evaluated—the PWM system consistently displayed shorter mean rate transition and stabilization times compared to the RC system. Overall, the findings from this study suggest PWM systems tend to be more responsive and effective, making them the preferred choice for efficient precision site-specific pesticide applications. Future research should evaluate the influence of other operational parameters such as look-ahead time and ground speed variations on the performance of both systems in actual field applications. Full article

The development of methods to detect plant stress is not only a scientific challenge, but is also of great importance for agriculture and forestry. However, at present, stress diagnostics based on plant spectral characteristics has several limitations: (1) the high dependence of stress assessment on plant species identity; (2) the poor differentiation of different types of stress; and (3) the difficulty of detecting stress before visible symptoms appear. In this regard, the development of plant spectral metrics represents a significant area of research. Ficus elastica plants were exposed under the photosynthetic photon flux density (PPFD) from 0 to 1200 μmol photons m⁻²s⁻¹. Exposure of F. elastica leaves to excess light (EL) (≥400 μmol photons m⁻²s⁻¹) resulted in an increase in reflectance in the yellow-green region (522–594 nm) and a decrease in reflectance in the red region (666–682 nm) of the spectrum, accompanied by a shift of the red edge point toward the longer wavelength. These changes were revealed using the previously proposed light stress index (LSI = mean(R_666:682)/mean(R_522:594)). Based on the results obtained, two new vegetation indices (VIs) were proposed: LSI_Red = R₆₇₄/R₆₅₄ and LSI_Norm = (R₆₇₄ − R₆₅₄)/(R₆₇₄ + R₆₅₄), indicating light stress by changes in the red region of the spectrum. The results of the study showed that LSI, LSI_Red, and LSI_Norm have a high degree of coupling strength with maximal quantum yields of photosystem II values. The plant response to EL exposure, as assessed by the values of these three VIs, was well expressed regardless of the PPFD levels. The effect of EL at non-stressful PPFDs (50–200 μmol photons m⁻²s⁻¹) was found to disappear within one hour after cessation of exposure. In contrast, the effect of the stressful PPFD (800 μmol photons m⁻²s⁻¹) was found to persist for at least 80 h after cessation of exposure. The results of the study indicate the need to consider light history in spectral monitoring of vegetation. Full article