Search Results (60)

In order to sustain food production under conditions of limited water and in arid regions using the least amount of irrigation water possible, two experiments were conducted during the years 2021 and 2022 in the Nubaria region, Egypt. The performance of an automated drip irrigation control system was evaluated as a potentially efficient and sustainable alternative to manual irrigation to increase the fertilization frequency (N P K) of lemon trees. This study underlines the importance of automatically applying and controlling the addition of irrigation water as a sustainable alternative to manual irrigation, while increasing the number of mineral fertilization times under sandy soil conditions to the largest possible number (12 times during the growing season of lemon trees) instead of three times. The application of automatic irrigation reduced the water stress on the roots of the lemon trees, in addition to increasing the efficiency of the addition. The latter led to the creation of a healthy environment in the area where the roots spread and increased the rate of absorption of irrigation water loaded with the necessary major elements, thus increasing the canopy volume of the lemon trees. This, in turn, led to an improvement in the efficiency of the photosynthesis process, resulting in an increase in the productivity, water productivity, and quality characteristics of lemon in sandy soil in dry areas. Increasing the number of times of mineral fertilization to 12 during the growing season led to a long-term increase in the concentrations of those minerals within the area of root spread, avoiding losing them by deep percolation, as occurs fertilization is carried out only three times per season. The highest values of the productivity and irrigation water saving were 47.6% and 47.4%, respectively, during the first season and 48.7% and 48.8%, respectively, during the second season. The highest values of water productivity and lemon fruit quality were also achieved under the same conditions. Therefore, this study recommends the automatic control of irrigation schedules, in addition to increasing the frequency of fertilization times, not only in lemon plantations, but also with most horticultural fruit trees grown in dry sandy lands. Full article

The online dynamic collection of irrigation and plant physiological information is crucial for the precise irrigation management of nutrient solutions and efficient crop cultivation in vegetable soilless substrate cultivation facilities. In this study, an intelligent weighing system was installed in a tomato substrate cultivation greenhouse. The monitored values from the intelligent weighing system’s pressure-type module were used to calculate irrigation start–stop times, frequency, volume, drainage volume, drainage rate, evapotranspiration, evapotranspiration rate, and stomatal conductance. In contrast, the monitored values of the suspension-type weighing module were used to calculate the amount of weight change in the plants, which supported the dynamic and quantitative characterization of substrate cultivation irrigation and crop growth based on an intelligent weighing system. The results showed that the monitoring curves of pressure and flow sensors based on the pressure-type module could accurately identify the irrigation start time and number of irrigations and calculate the irrigation volume, drainage volume, and drainage rate. The calculated irrigation amount was closely aligned with that determined by an integrated-water–fertilizer automatic control system (R² = 0.923; mean absolute error (MAE) = 0.105 mL; root-mean-square error (RMSE) = 0.132 mL). Furthermore, transpiration rate and leaf stomatal conductance were obtained through inversion, and the R², MAE, and RMSE of the extinction coefficient correction model were 0.820, 0.014 mol·m⁻²·s⁻¹, and 0.017 mol·m⁻²·s⁻¹, respectively. Compared to traditional estimation methods, the MAE and RMSE decreased by 12.5% and 15.0%, respectively. The measured values of fruit picking and leaf stripping linearly fitted with the calculated values of the suspended weighing module, and R², MAE, and RMSE were 0.958, 0.145 g, and 0.143 g, respectively. This indicated that data collection based on the suspension-type weighing module could allow for a dynamic analysis of plant weight changes and fruit yield. In summary, the intelligent weighing system could accurately analyze irrigation information and crop growth physiological indicators under the practical application conditions of facility vegetable substrate cultivation, providing technical support for the precise management of nutrient solutions. Full article

The automatic flushing valve (AFV) enables automatic flushing of drip irrigation systems, improving their anti-clogging performance. This study focuses on a subsurface drip irrigation system (SDI) for alfalfa, selecting T20 and T70 AFVs (with designed flushing durations of 20 and 70 s, respectively) installed at the end of the dripline and a buried dripline without an AFV as a control. The aim of this study was to explore the variations in AFV hydraulic performance over two years of operation and the impact on the irrigation uniformity of SDI systems. The results revealed that the flushing duration (FD) and flushing water volume (FQ) of both T20 and T70 fluctuated over time, with an average coefficient of variation (CV) of 13.2%. The FD and FQ of the two types of AFVs are affected by the daily average temperature (T), and when T increases from 20.1 °C to 25.7 °C, the FD and FQ increased by an average of 22.6%. After 2 years of operation, the average relative flow rate (Dra) and irrigation uniformity (Cu) of the T20 and T70 SDI emitters were 93.7% and 96.8%. Both the Dra and Cu were significantly influenced by FD (p < 0.05). Compared with CK and T20, T70 significantly increased the Dra and Cu by 6.3% and 4.6%, respectively. The order of degree of clogging at different positions in the dripline was rear > middle > front for the CK and T20 treatments, whereas for T70, it was middle > front > rear. With the installation of the T70 AFV, the time required for the SDI system to reach moderate clogging (Dra = 50~80%) was extended from 3~7 years to 8~20 years, resulting in a 180% increase in operation time. The T70 AFV is recommended for use in the alfalfa SDI of this study. Full article

Drip irrigation pressure is considered a key parameter for controlling and designing the drip irrigation system in sandy soils. Understanding soil water content (SWC) movements under varying pressures can enhance water use efficiency and support sustainable irrigation strategies for crops in arid regions. The objectives of this study were to investigate the effects of irrigation pressure on the spatial, vertical, and temporal variability of SWC in sandy loam soil using surface drip irrigation. Experiments were carried out in a soil bin located in a greenhouse. SWC sensors were placed at depths 10, 20, 30, 40, and 50 cm to monitor SWC variability under low, medium, and high drip irrigation pressures (25, 50, and 75 kPa) at a constant emitter flow rate of 3 L/h. A pressure controller was used to regulate drip irrigation pressure, while microcontrollers communicated with SWC sensors, collected experimental data, and automatically recorded the outputs. At low irrigation pressure, water content began to increase at 0.53 h and saturated at 3.5 h, with both values being significantly lower at medium and high pressures. The results indicated that lower pressures led to significant variability in water movement at shallow depths (10 to 30 cm), becoming uniform at deeper layers but requiring longer irrigation times. Competitively higher pressures showed uniform water distribution and retention statistically throughout the soil profiles with shorter irrigation times. The variation in water distribution resulting in non-uniform coverage across the irrigated area demonstrates how pressure changes affect the flow rate of the emitter. The results provide information maps with soil water data that can be adjusted with irrigation pressure to maximize water use efficiency in sandy loam soils, aiding farmers in better irrigation scheduling for different crops using surface drip irrigation techniques in arid environments. Full article

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices. Full article

The automation of agriculture can transform farming from manual to dynamic, resulting in higher profits with less manual management. This article introduces the use of automatic irrigation to monitor and control soil moisture through automatic irrigation. The control unit is implemented by an ATMEGA328P microcontroller on the Uno platform. This device uses a hygrometer to measure actual humidity. This benefit ensures that the system uses water correctly, thus preventing excess/underwater. IoT is used to inform farmers about the status of the water supply. Sensor data are updated regularly via the GSM-GPRS website, and farmers can check whether the water head is open/closed at any time via the website. Sensor readings are transmitted to the object’s audio channel to create an image. Full article

Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions. Full article

Efficient irrigation water use directly affects crop productivity as demand increases for various agricultural products due to population growth worldwide. While technologies are being developed in various fields, it has become desirable to develop automatic irrigation systems to reduce the waste of water caused by traditional irrigation processes. This paper presents a novel approach to an automated irrigation system based on a non-contact computer vision system to enhance the irrigation process and reduce the need for human intervention. The proposed system is based on a stand-alone Raspberry Pi camera imaging system mounted at an agricultural research facility which monitors changes in soil color by capturing images sequentially and processing captured images with no involvement from the facility’s staff. Two types of soil samples (sand soil and peat moss soil) were utilized in this study under three different scenarios, including dusty, sunny, and cloudy conditions of wet soil and dry soil, to take control of irrigation decisions. A relay, pump, and power bank were used to achieve the stability of the power source and supply it with regular power to avoid the interruption of electricity. Full article

Innovation has been the transforming tool of precision agriculture as a response to population growth and the demand for more food with quality, less waste, food security, and sustainable management of environmental resources. The challenges are to increase the productivity of cultivated areas, both for current and future areas, to manage the use of potable water, scarce in many regions, to keep the soil fertile, and to reduce waste through reuse, optimization, resource sharing, and operational and strategic management based on accurate information of planting, harvesting, and management of environmental conditions, which are also objectives of the Circular Economy. Therefore, using Industry 4.0 technologies in agriculture becomes fundamental to facing such challenges. This paper presents a systematic literature review on Industry 4.0 technologies adopted in agriculture for sustainable development, considering environmental, economic, and social benefits. The research pointed to the use of IoT in irrigation control systems by sending automatic commands, monitoring soil and weather conditions, in the use of machinery with some automation features and in cloud data storage systems, and with the use of Big Data analytical tools, with access by mobile devices, these uses contribute to operational and strategic decision making in the management of planting and harvesting. However, the literature review did not find a technological architecture for Integrated Services in Agriculture 4.0. Thus, this paper proposes a Service Architecture that enables the promotion of a Circular Economy in Agriculture 4.0. The contribution of this article to the theory is in the expansion of knowledge of the use of technologies in Agriculture 4.0. In terms of practice, this article provides an Integrated Service Architecture so that new products can be developed for Agriculture 4.0 and thus contribute to society in reducing food insecurity, generating environmental, economic, and social benefits, and promoting the Circular Economy in Agriculture 4.0. Full article

Shortages of water resources and labor make it urgent to improve irrigation efficiency and automation. To respond to this need, this study demonstrates the development of an adaptive border irrigation system. The inflow is adjusted based on the functional relationship between the advance time deviation and the optimal adjustment inflow rate, thereby avoiding the real-time calculation of infiltration parameters required by traditional real-time control irrigation systems. During the irrigation process, the inflow rate is automatically adjusted based only on the advance time deviation of the observation points. The proposed system greatly simplifies the calculation and reduces the requirements for field computing equipment compared with traditional real-time control irrigation systems. Field validation experiments show that the proposed system provides high-quality irrigation by improving the application efficiency, distribution uniformity, and comprehensive irrigation performance by 11.3%, 10.7%, and 11.0%, respectively. A sensitivity analysis indicates that the proposed system maintains a satisfactory irrigation performance for all scenarios of variations in natural parameters, flow rates, and border length. Due to its satisfactory irrigation performance, robustness, facile operation, and economical merit compared with traditional real-time control irrigation systems, the proposed system has the potential to be widely applied. Full article

This paper proposes a new automatic tuning method for the proportional-integral (PI) controllers of photovoltaic irrigation systems (PVIS) without the need for any other power source or batteries. It enables the optimisation of the values of the PI parameters ($K_{\mathrm{p}}$ and $K_{\mathrm{i}}$) automatically, eliminating the requirement for skilled personnel during the installation phase of PVIS. This method is based on the system’s voltage response when a disturbance signal is introduced through the feedforward input of the PI controller. To automatically assess the response properties, two indicators are proposed: the total harmonic distortion (THD), used to evaluate the sine response, and the total square distortion (TSD), used to evaluate the square response. The results indicate that the tuning changes for different irradiance and temperature conditions due to the non-linearity of the system, obtaining the most conservative values at maximum irradiance and temperature. The robustness of the results of the new automatic tuning method to abrupt photovoltaic (PV) power fluctuations due to clouds passing over the PV generator has been experimentally tested and the results show that the obtained tuning values make the PVIS stable, even when PV power drops of 66% occur abruptly. Full article

The problem: Single-incision surgery is a complex procedure in which any additional information automatically collected from the operating field can be of significance. While the use of robotic devices has greatly improved surgical outcomes, there are still many unresolved issues. One of the major surgical complications, with higher occurrence in cancer patients, is intraoperative hemorrhages, which if detected early, can be more efficiently controlled. Aim: This paper proposes a hazard detection system which incorporates the advantages of both Artificial Intelligence (AI) and Augmented Reality (AR) agents, capable of identifying, in real-time, intraoperative bleedings, which are subsequently displayed on a Hololens 2 device. Methods: The authors explored the different techniques for real-time processing and determined, based on a critical analysis, that YOLOv5 is one of the most promising solutions. An innovative, real-time, bleeding detection system, developed using the YOLOv5 algorithm and the Hololens 2 device, was evaluated on different surgical procedures and tested in multiple configurations to obtain the optimal prediction time and accuracy. Results: The detection system was able to identify the bleeding occurrence in multiple surgical procedures with a high rate of accuracy. Once detected, the area of interest was marked with a bounding box and displayed on the Hololens 2 device. During the tests, the system was able to differentiate between bleeding occurrence and intraoperative irrigation; thus, reducing the risk of false-negative and false-positive results. Conclusion: The current level of AI and AR technologies enables the development of real-time hazard detection systems as efficient assistance tools for surgeons, especially in high-risk interventions. Full article

High-performance fertilization equipment with high uniformity is essential for the improvement of fertilizer use efficiency in paddies. The performance of these fertigation systems might be affected by the initial field conditions and fertilizer doses. In this study, the uniformity of fertilization by an automatic system (SF) was investigated; the investigation had two initial field water conditions and fertilizer doses, and manual fertilization by farmers (FF) was used as the control. After fertilization, the Christiansen uniformity coefficient (CU) in the SF paddies was higher than in the FF paddies, and the SF in the non-flooded paddies (SFN) was the highest. With time, the CU of treatments with poor fertilization uniformity was improved; it was driven by the osmotic potential of fertilizer ions, but it was far from exceeding that of the treatments originally conducted with higher CU. For the SF treatments, the fertilizer dose did not affect fertilization uniformity significantly; so, an SF can match the efficient fertilization strategies more precisely. As water-saving irrigation (WSI) is conducive to the production of non-flooded field conditions and the promotion of the efficient use of topdressing, the use of automatic fertilization systems to implement efficient fertilization management practices in WSI paddy fields is suggested. Full article

The aim of this note is to provide a quick overview of the installation and adjustment of an exclusively mechanical standalone automatic device that self-adjusts to weather changes to control the frequency and duration of the irrigation. The “hydraulic evapotranspiration multisensor” (HEM) is composed of a reduced evaporation pan with water, a magnet with a floater floating in the pan, a hydraulic device operated by a magnetic hydraulic valve that has the ability to adjust the frequency of irrigation, and a hydraulic system that returns water to the pan during each irrigation event through an adjustable dripper to replace the water lost due to the fact of evaporation. This note is particularly relevant for arid–semi-arid regions where agricultural production is fully dependent on irrigation. Full article

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%. Full article