Large Irrigated Areas Research Articles

QUANTITATION OF RETURN COLLECTOR-DRAINAGE WATER FROM THE IRRIGATED AREAS IN ARID ZONES

The article deals with the study of the dependence of return collector-drainage water from the large irrigated areas on the volume of water withdrawal for irrigation purposes and the total area of crops. The task is solved in the context of irrigated lands in the Bukhara region of Uzbekistan. Three variants of calculation were performed using an objective method of equalizing and normalizing correlations. Equations of normalized regression are obtained that characterize the relationship between the collector-drainage runoff, the volume of water intake from the Amu-Bukhara canal and the area of irrigated land. Their total multiple correlation coefficients are rather high. Based on the third variant of these equations, a calculated nomogram was plotted. The accuracy of the nomogram was assessed. The results showed that it can be used both for calculation and forecast of collector-drainage runoff from the large irrigated land areas in arid zones.

Convection Initiation Associated with a Boundary Layer Convergence Line over a Real-World Sharp Vegetation-Contrast Area

Abstract Convection initiation (CI) has remained a major challenge in weather forecasting worldwide. The Hetao area in North China, the location of Asia’s largest irrigation area, contains highly heterogeneous vegetation where near-surface convergence lines (boundaries) parallel to the oasis–desert border often emerge over the desert and initiate convection. This study investigated the evolution of such a boundary and its influence on the CI process where a series of cells were successively initiated along the boundary on 4 June 2013. Our results indicated that uneven surface heating across the oasis–desert border produced mesoscale thermal circulation. The westerly oasis breeze in the thermal circulation converged with the southerly background wind and formed a boundary over the desert along the high-temperature contrast line. A middle-hemisphere westerly trough further enhanced uplift and facilitated CI. Our simulation revealed that the first 30-dBZ parcels in each cell originated from either the desert side at a low level or the oasis side at a middle level, rather than from the oasis at a low level, as indicated by previous idealized studies. Southerly low-level parcels veered above the boundary and experienced a longer lifting time over the desert, while western parcels originating from the oasis experienced a shorter lifting time and smaller vertical displacement, resulting in middle-level parcels instead of low-level parcels that reached their level of free convection. Even though CI occurred over a surface boundary without a near-surface stable layer, the inflow may have originated from middle levels rather than in contact with the surface. Significance Statement The purpose of this study is to understand the evolution of a real-world near-surface convergence line and the associated deep convection initiation (CI) along the border of Asia’s largest irrigation area and the desert in northern China. Notably, previous works have mainly focused on shallow convection over uneven vegetation distributions based on idealized simulations, which may be quite different from real-world interactions between thermal circulation and background flow. Our results highlight different parcel sources in different convections initiated by the same convergence line, which is different from the idealized situation where parcels mainly originate from the low-level oasis side.

Comparing machine learning and deep learning classifiers for enhancing agricultural productivity: case study in Larache Province, Northern Morocco

<p><span lang="EN-US">The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses.</span></p>

Multi-layer multi-objective cooperative regulation of agricultural water resources in large agricultural irrigation areas based on runoff prediction

Reasonable and optimal allocation of irrigation water is of great significance to improve agricultural water use efficiency for large-area agricultural irrigation systems. To solve the problems of large agricultural production scale, unreasonable water distribution structure, many rivers in the region and large changes in runoff volume, this paper takes the Sanjiang Plain in Heilongjiang Province, China, as the research object to develop an efficient and accurate water optimal allocation method. First, the runoff volume of many rivers in the region was obtained through the ARIMA-SVM-BP combined runoff prediction model, based on which a ‘regional-irrigation district-farmland’ multilayer multiobjective water resource cooperative deployment system model was established to achieve the synergistic enhancement of farmers' interests and the economic-social and regional economic-engineering volume of the irrigation district. The joint optimization method, fuzzy mathematical planning, and large system decomposition-coordination coupling were used to solve the multilayer multiobjective regulation system. The results show that the optimal agricultural water allocation volumes for the current year (2020) and the forecast year (2025) of 37 irrigation districts in the Sanjiang Plain are 3.61 × 109 m3 and 3.89 × 109 m3, saving 17% of water and resulting in a 24% decrease in the agricultural water use ratio compared with the actual situation, respectively. In the process of repeated coordination of the large system, the water distribution error is controlled within 1%. The fairness of water distribution in the multilayer system was improved by 34.7% while obtaining high economic benefits. The method proposed in this study helps the coordinated regulation and planning of multiple water sources in large agricultural irrigation areas, improves the ability of water distribution in irrigation areas to cope with different levels of incoming water, and promotes the sustainable development of regional irrigated agriculture.

A HISTORICAL OVERVIEW OF EVOLUTION OF THE IRRIGATED AGRICULTURE SECTOR IN MOROCCO

Purpose and aim of the study: The paper aim is to analyse scientific and other relevant literature on the sustainable development of the irrigated agricultural sector in Morocco.Design / Methodology / Approach: The method of data collection is literature review. Literature review was chosen as it is the basis for further research. The obtained data is presented in accordance with the identified historical periods. A summary of results is delivered.Main Findings: In light of scarce water resources, the technical, legal and institutional levels in the agricultural sector are permanently updated by the public authorities in order to mitigate the impact of climate change and adapt the management of water resources.Originality: Sustainability is regarded as gradual and sequential growth and development. Sustainability means the consideration of all the stakeholders’ interests in the decision-making process.Implications: Concerted efforts of the public authorities, companies and other stakeholders in the improvement of the efficiency of water use in agriculture through the development of localized irrigation technology and modernization of collective or private water supply networks in large irrigated areas would be beneficial for all. Innovative irrigation systems that allow the exploitation of unconventional, largely unused water resources must be developed. Low cost solutions with natural and locally available materials (Low Technology, Low Energy, Low cost, Easy to Use) should be used.

Smart Irrigation system on Agriculture Fields Using IOT

Irrigation is the process of supplying water to land and soil, which is also helpful in growing of agricultural crops, maintenance of landscapes in agricultural field. Modern technology has been improved for inadequate rainfalls and improvement of irrigation system has also justified minimizing the waste of water in which it also reduces works in agricultural fields. This also makes people to work conveniently and also reduces work load. As we know China and India have largest irrigation area that is 21.3% and 20.6% respectively. Malaysia is one of agricultural oriented country. Irrigation is taken care of agricultural fields which involve supplying water to land to assist for production of crops. Soil moisture which is nothing but the content of the soil cannot be maintained efficiently. So that automation irrigation system would be better solution for this. The soil moisture sensor which measures the content in the soil and the system will start automatically when the moisture level is low.

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles.

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with and RMSE CV = 0.88 m2 m-2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloudprone agri-environments.

Effects of precipitation, irrigation, and exploitation on groundwater geochemical evolution in the people’s victory canal irrigation area, China

The People's Victory Canal Irrigation Area is a large gravity irrigation area in the middle and lower reaches of the Yellow River. Groundwater hydrochemistry is controlled by geochemical weathering, precipitation, irrigation from the Yellow River diversion, and artificial exploitation. The Piper diagram, hierarchical cluster analysis, Gibbs diagram, and ion proportion coefficient were applied to improve our understanding of the hydrogeochemical evolution. Our results indicated that groundwater ion concentration has prominent spatial zonation characteristics in the study area. The groundwater ion components in the low salinity area are mainly a result of silicate and carbonate weathering, with the major hydrochemical type being HCO3·Cl–Na·Mg and HCO3–Na·Ca. The soil salt content in the low salinity area is low. The main effect of precipitation on groundwater chemical composition is a reduction in groundwater ion concentration by recharge. The groundwater ion concentration near the canal is similar to the Yellow River water. The groundwater ionic components in the high salinity area are mainly affected by weathering, silicate dissolution, and evaporative salt rocks. The water chemical type is SO4·Cl–Na·Mg. Expectedly, the soil salt content is high in the high salinity area. The Yellow River recharge decreased the ion concentration of groundwater near the canal. In addition, the groundwater exploitation increase results in a change in the flow field, affecting the local groundwater chemistry. The groundwater ion concentration increases as the groundwater flow from the high salinity area to the low salinity area. In contrast, the groundwater ion concentration in the high salinity area decreases. We further discuss these results in the context of the complex influence of precipitation, Yellow River diversion irrigation, and artificial exploitation on the hydrogeochemical evolution.

Distribution, Sources, and Risk of Polychlorinated Biphenyls in the Largest Irrigation Area in the Yellow River Basin

To investigate the contamination of PCBs in agricultural soils irrigated chronically with polluted water and the distribution and migration of PCBs under long-term irrigation, 100 farmland soil profile samples were collected in the Yellow River irrigation area in Inner Mongolia, China, to determine PCB content. Cluster analysis was used to identify possible sources of PCBs products, and the USEPA Health Risk Evaluation Model assessed the health risks posed by PCBs to humans. The results showed that the detection rates of eight monomers in the different soil layers of each sample site ranged from 5% to 90%, and the concentration ranged from not detected to 87.71 ng·g−1. The PCBs content showed a vertical distribution rule of accumulation in the shallow layer, sudden decrease in the middle layer. Low-chlorinated PCBs were dominant in each soil profile. Source identification indicated that PCB pollution in the study area originated mainly from the Aroclor1242, Aroclor1248, Aroclor1016, Aroclor1232, and Aroclor1221 industrial products and domestic transformer oil. Finally, a health risk assessment demonstrated that child and adult groups in study area were exposed to negligible carcinogenic and noncarcinogenic risks.

Atmospheric recycling of agricultural evapotranspiration in the Tarim Basin

Precipitation recycling, defined as that precipitation in a region is partially contributed by evapotranspiration from the same region, is the interaction between terrestrial hydrology and atmospheric processes, and plays a crucial role in forming water resources. For agricultural lands, the evapotranspiration of irrigation water alters local climate by reprecipitation in surrounding regions, which can also be analyzed from the viewpoint of precipitation recycling. As the largest endorheic basin in China, the Tarim Basin has strong precipitation recycling contributed by the surrounding mountains, as well as large irrigation areas producing high-quality cotton, which makes it an appropriate study case for the recycling of irrigation water. In this paper, we calculate the water vapor sources of precipitation (WSoP) and the reprecipitation of evapotranspiration (RPoET) in the Tarim Basin by using the Water Accounting Model—Two Layers, analyze their spatial distributions, and find the moisture recycling ratio of the basin as 15.4%. We set up comparative scenarios of evapotranspiration increase by irrigation areas in different locations of the Tarim Basin, and study their difference in reprecipitation and moisture recycling. Results show that the evapotranspiration increase in different locations has a marginal reprecipitation ratio ranging from 8% to 24%, and further boosts the whole basin’s moisture recycling ratio by 0.11%–0.29%. Significant difference among the scenarios proves that the location of irrigation areas affects the reprecipitation of its evapotranspiration, and the optimized moisture recycling can benefit water resource and ecosystem conditions inside the basin. In summary, this work would be useful to provide a practical basis for irrigation planning by considering the land-atmosphere interaction.

Результаты исследований окарбоначивания почв орошаемых участков по материалам дистанционного зондирования Земли

The authors discuss a soil carbonate enrichment problem in the Volgograd region. These processes cause a decrease in yield and require timely identification and assessment of manifestation extent. Observing of large irrigated areas is very difficult. Therefore, the possibilities of monitoring soil carbonate enrichment processes using the analysis of Earth remote sensing data are becoming relevant. The aim of this work is to research the relationship of soil carbonate enrichment with the spectral characteristics of the surface obtained from the data of the “Resurs-P” ultra-high resolution satellite. Using the image from “Resurs-P”, the standard spectral indices were calculated. Most effective ones were selected, having the highest correlation with the degree of soil carbonization, determined from the compared field samples. Through geographic information systems, space images were marked with areas at the sampling points. Information from those sites was then analyzed using graphical visualization and regression analysis. As a result, a relationship was established between the values of the areas’ spectral brightness in the image from the “Resurs-P” and the level of carbonate enrichment. Based on the identified relationships, the Random Forest classifier was trained, using which a map of carbonated soils distribution in the irrigated territory of the Svetloyarsk irrigation system’s test site was created.

Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery.

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m−2, CCC: R2 = 0.80, RMSE = 0.27 g m−2 and VWC: R2 = 0.75, RMSE = 416 g m−2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.

Contamination, transport, and ecological risks of pharmaceuticals and personal care products in a large irrigation region

Pharmaceuticals and personal care products (PPCPs) have attracted widespread attention owing to their extensive use and potential adverse effects on human and ecosystem health. There is a lack of information regarding the occurrence and environmental fate of PPCPs in large agricultural irrigation areas in China. In this study, we conducted a comprehensive survey on 30 PPCPs in water from Hetao Irrigation District, one of the three largest irrigation areas in China. The ΣPPCP-concentrations ranged 82.13–1409.24 ng/L in August and 40.53–887.20 ng/L in November, with caffeine (CAF), norfloxacin (NOR), erythromycin (ERY), sulfamethoxazole (SMX) and ofloxacin (OFL) being the predominant compositions. Spatially, the average ΣPPCP concentrations increased from irrigation to drainage water, and then decreased in Wuliangsuhai Lake. Less PPCP mass loading (55.05 kg/y) migrated from Wuliangsuhai Lake to Yellow River than that from the Yellow River to Hetao Irrigation District (425.88 kg/y), indicating that Wuliangsuhai Lake plays an important role in improving water quality. An ecological risk assessment showed that it is worthwhile to consider the presence of CAF, ERY, NOR, and OFL in natural surface water and to control their potential risks.

Technical assessment of an equatorial Low Earth orbit satellite constellation for Egypt

Egypt, the region’s agricultural powerhouse, has the largest irrigation area among the eleven Nile Basin countries. Meanwhile, the demand for water and irrigation management is becoming necessary. The Internet of Things (IoT) plays a key role in smart agriculture and water management to cope with modern water scarcity. This paper presents the methodology of design and assessment of a small IoT satellite constellation providing continuous coverage over Egypt and decides on the optimal location for ground stations to serve IoT sector in Egypt. Moreover, it evaluates the latency analysis and handover time for such constellation.It is shown that a constellation of 9 satellites in a circular equatorial Low Earth Orbit (LEO) can provide continuous coverage for Egypt. The latency analysis showed that two ground stations in southernmost Egypt will provide the optimal latency for such constellation.

Transport and environmental risks of perfluoroalkyl acids in a large irrigation and drainage system for agricultural production.

The quality of irrigation water and drainage water is essential for local ecosystem and human health in agricultural regions. In this study, the transport analysis, source identification, and environmental risk assessment of perfluoroalkyl acids (PFAAs) were conducted in the largest irrigation area in northern China. The concentrations of the total PFAAs (ΣPFAA) ranged from 41.5 to 263ng/L in surface water, and the short-chain perfluoroalkyl carboxylic acids (PFCAs) and perfluorooctanoic acid (PFOA), were dominant with a total contribution of 94%. Generally, the ΣPFAA levels increased from irrigation waters to drainage and receiving lake waters. PFOA showed the highest increase, with potential emission sources located in the catchment of the sub main drainage ditch D5. More PFOA (36.8kg/y) was outflowed from Ulansuhai Lake to the Yellow River than that inflowed from the Yellow River to the irrigation district (6.15kg/y). The results of a risk assessment indicated that avian wildlife living in Ulansuhai Lake were threatened by the PFOA and perfluorobutane sulfonate (PFBS) pollution. The estimated daily intakes (EDIs) of the sum of the PFOA, perfluorononanoic acid (PFNA), perfluorohexane sulfonic acid (PFHxS), and perfluorooctane sulfonic acid (PFOS) through aquatic food consumption for people with the different aquatic food preferences accounted for 6-42% (urban) and 4-27% (rural) of the strictest tolerant daily intake (TDI) value. The results of this study highlight the impact of local emissions of PFAS on massive irrigation and drainage systems, and ultimately, the ecosystem and human health.

Smart Control and Energy Efficiency in Irrigation Systems Using LoRaWAN.

Irrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers a large coverage of up to 5 km, has low power consumption and does not need additional hardware such as repeaters or signal amplifiers. This research develops a control and monitoring system for irrigation systems. For this purpose, an irrigation algorithm is designed that uses rainfall probability data to regulate the irrigation of the installation. The algorithm is complemented by checking the sending and receiving of information in the LoRa network to reduce the loss of information packets. In addition, two temperature and humidity measurement devices for LoRaWAN (THMDLs) and an electrovalve control device for LoRaWAN (ECDLs) were developed. The hardware and software were also designed, and prototypes were built with the development of the electronic board. The wide coverage of the LoRaWAN allows the covering of small to large irrigation areas.

Environmental and human-health risks of As in soils with abnormal arsenic levels located in irrigated agricultural areas of Paracatu (MG), Brazil

The municipality of Paracatu (Brazil) is notorious for its large irrigated agricultural area and by abnormal arsenic (As) levels in selected soils of the region. Concerns regarding As exposure via ingestion of water and food are frequent, yet little is known about the behavior of arsenic in irrigated agricultural soils, as well as on As bioaccessibility/bioavailability in agroecosystems of this region. This work evaluated total and available As in agricultural soils cultivated under irrigation and in soils under native vegetation in Paracatu. We also assessed reactive arsenic fractions and As bioaccessibility in the soil, as well as arsenic levels in plant shoots to estimate As risks in these agroecosystems. Soil (different depths) and plant tissue samples were collected in 6 irrigated agricultural areas (CA1 to CA6) and 4 reference areas (RA1 to RA4). Total soil-As did not differ between soil depths, reinforcing that the source of As in agricultural soils is natural. This was evident when counterpointing arsenic and phosphorus contents at different soil depths, as both accumulate on the surface of oxidic soils when added to agroecosystems by anthropogenic routes (e.g., phosphate fertilization for P and irrigation for As). Available As levels in soils and plants were very small (below detection limit). Furthermore, all soils presented very low oral As bioaccessibility. Our findings revealed that the irrigated soils are not As polluted due to the low enrichment and accumulation of arsenic, as well as the prevalence of low ecological risks. There is no non-carcinogenic risk for the local population, except for children in RA2. The estimated carcinogenic risk for children followed the order RA2 > CA3 > CA4 > RA3 > CA2, and for adults, RA2 > CA3. Ultimately, the strategy of comparing the behavior of P and As in the soils of this study proved to be efficient in showing that there are no major risks to humans and the environment in the investigated area. However, periodic monitoring of As bioavailability in these areas is recommended.

Modeling the effects of irrigation management scenarios on winter wheat yield and water use indicators in response to climate variations and water delivery systems

Agriculture is the major user of the available fresh water resources at the global scale. However, water resources are negatively affected by climate change. Therefore, agricultural water management is globally considered as a key solution to address the current and future water resources challenges. In this study, a large number of recommended and probable scenarios of different deficit irrigations, climatic conditions and rainfall patterns, and sowing dates were defined and modeled under Rotational and On-demand irrigation scheduling (IS). For this purpose, the AquaCrop model that was calibrated and validated based on the observed data of 15 growing seasons was applied on winter wheat in a warm and semi-arid environment in the south-west of Iran. The AquaCrop model simulated grain yield accurately with normalized root mean square error (NRMSE) of 5% and 6% for calibration and validation, respectively. Analyses showed that the yield response factor in the different scenarios was higher than 1 in all climatic conditions that indicates high wheat sensitivity to water stress in the study area. Besides, analyzing the optimal irrigation plans for all possible climatic conditions and deficit irrigation levels showed that irrigation at the stem elongation stage was the most critical irrigation event. Further analysis also showed that November 8th − 22nd is the optimal window for achieving the highest grain yield in all climate conditions. Modeling showed that the Rotational IS is more adaptable to deficit irrigation practices due to maintaining higher grain yield and water productivity compared with the On-demand IS. Indeed, it is possible to maintain grain yield and increase water productivity by applying deficit irrigation up to 40% of full irrigation under Rotational IS. In conclusion, the results of this scenario-based modeling study showed that achieving the highest grain yield and water productivity in a large irrigation area depends on a combination of deficit irrigation scenarios and the water delivery systems in response to climatic variations. However, changing the water delivery systems from Rotational to On-demand requires expensive infrastructure investment and irrigation scenarios analysis before any decision.

Automatic water level monitoring system based on computer vision technology for supporting the irrigation modernization

Measurement of water level in the irrigation channel is required to calculate the irrigation discharge so that crops can be irrigated precisely. Large irrigation areas require a real-time and accurate monitoring system that cuts operational costs. In a previous study, ultrasonic technology has already been developed and implemented. However, during the actual operation, visual verification is needed. To address the problem, an automatic water level monitoring system based on visual appearance using computer vision technology is developed. The image-based system works by automatically capturing canal gauge, identifying canal gauge in an image using colour detection method, measuring the length of the pixel of canal gauge, and convert it to an actual water level. As a result of the performance test, five photos were taken in 24 hours. One image was accurately measured by the microcomputer. This is because the intensity of sunlight affects the colour of the canal gauge so that the microcomputer incorrectly or inaccurately identifies the canal gauge length. A pre-calibration is needed to find a specific colour range for specific light intensity. Also a robust, less colour sensitive such as night vision imagery needs to be developed to overcome luminescence problems.

A Prototype Modeling of Smart Irrigation System Using Event-B

Traditional irrigation operates on a preset programmed schedule and timers. Smart irrigation monitors weather, soil conditions, evaporation, and plant water use to automatically adjust the watering schedule to actual conditions of the site. Deployment of IoT based sensors, GPS systems, and usage of solar energy greatly contributes to the cause. In order to monitor large irrigation area with different harvests, seasons, and parameters demand a formal model for higher yields. In this paper, a prototype of a smart irrigation system is modeled using Event-B, and verification is done by the RODIN tool support.