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Okra is a well-known and economically important vegetable grown in Bangladesh for consumption and seed production. The yield of okra is low compared to developed countries that can be increased using irrigation water efficiently. Traditional irrigation methods are used for okra cultivation in the developing countries, resulting in significant water loss. One of the options that appears to be promising for reducing this water loss is drip irrigation since it applies water directly to the plant’s root zone. However, farmers frequently complain about manual inspection and water wastage from the overhead tanks in small-scale microirrigation systems. An automated microirrigation system can help to solve this problem. This approach was developed at Bangabandhu Sheikh Mujibur Rahman Agricultural University to evaluate the performance of okra in terms of plant growth, seasonal water usage, and yield factors. For a better understanding of specialist crop irrigation management, the effectiveness of water utilization was also investigated. The experimental field was set up in a Randomized Complete Block Design (RCBD) from March to May 2021, with three different treatments such as T1 (Conventional irrigation), T2 (Drip irrigation) and T3 (Timer drip irrigation), for comparing the treatments with four replications. To construct an automated microirrigation system, a pump controller with sensing probes to regulate the water level in the irrigation tank and irrigation timers for watering the okra field depending on scheduling were used. According to the data, only the length of the pod varied significantly, whereas the other factors were non-significant. The yield components, such as the weight of pod per plant and yield, did not varied significantly among the treatments. The highest (12.95 kg/m3) and the lowest (10.47 kg/m3) water use efficiency were obtained in T3 (Timer drip irrigation) and T1 (Conventional irrigation) respectively. The study reveals that automated microirrigation technology can save 11% more irrigation water than conventional irrigation.

Concentrations of heavy metals in irrigation water and soil were examined to determine the level of contamination in a particular irrigation channel in Gazipur, Bangladesh. In this conduit, effluent from households, industries, and municipalities was combined with irrigation water. The study was carried out in order to estimate pollutant loads along a wastewater irrigation channel and to conceptualize seasonal changes in composite wastewaters. A total of six wastewater and nine contaminated soil samples were collected directly from the study area and analyzed for this project. From February to March, before the monsoon, the pH of the water is too low for this channel to be used for irrigation. At the end of the monsoon season, from April through May, all wastewater tests were within these parameters, making them suitable for irrigation. Considering the standard limits, Pb, Cr, and Cu concentrations in all wastewater samples were found unsuitable for irrigation and thus problematic for soils and crops. The concentration of Cd in the wastewater samples ranged from 27.82 to 88.36 mg/L, whereas the tolerance limit for Cd in irrigation water is 0.01 mg/L. In soil samples from the three places chosen, the levels of Pb were below the safe limit and the levels of Cr were above the safe limit. The heavy metal pollution index of all the sites crossed the safe limit. On the basis of the geo-accumulation index and the pollution index, Pb indicates no pollution and Cd indicates very high levels of pollution in all locations. The maximum enrichment factor was found for Cd, Pb, and Cu, which have minor enrichment, Ni, which has no enrichment, and Cr, which has moderate enrichment. The study also tried to link heavy metals in wastewater to their deposition in soil and seasonal and temporal variations. The study revealed that industrial effluent utilized for irrigation was closely related to the accumulation of heavy metals in soils. Since irrigated agricultural soils are likely to be exposed to harmful wastes, it is crucial that the effluents from the industrial region of Gazipur are appropriately controlled.

Reference crop evapotranspiration (ETo) is essential for planning and management of irrigation to ensure optimum utilization of a region’s available water resources. ETo being an indicator of atmospheric evaporative demand provides a measure of the integrated effect of climatic parameters like solar radiation, wind, temperature and humidity. Variation of these climatic parameters over long period of time alters ETo. The modified ETo is crucial for periodic adjustment of irrigation planning and management. This study evaluated variation of ETo and contribution of the climatic parameters to ETo-variation in Mymensingh region of Bangladesh by analyzing climatic data of 28 years (1990–2017). ETo was determined by FAO Penman-Monteith method and trends of ETo and its governing climatic parameters were evaluated by MAKESENS trend model. The ETo-governing climatic parameters revealed contrasting trends, which also varied in different months of the year. Net radiation and wind speed showed decreasing trend, while temperature and saturation vapor pressure deficit showed increasing trend. In spite of contrasting contributions of the climatic parameters, their combined effect reduced ETo with a resulting decreasing trend of the monthly average daily ETo over the months of the year except July. These results enhance our understanding of the effects of climate change on ETo and can help correct-planning of water resources for irrigated agriculture. J. Bangladesh Agril. Univ. 17(2): 258–264, June 2019

Manual grading and sorting of cardamom spices require a long time and considerable resources. The introduction of machine vision technology can substantially increase the timeliness of grading operation and reduce the associated drudgery. This research was carried out to contribute towards the use of machine vision technology in the grading of cardamom spices. In this regard, the utility of images captured by mobile devices was assessed using digital image processing techniques. The color images of cardamom capsules were acquired using an Apple iPhone 7 in the first place. The geometric features (major diameter, minor diameter, surface area, and perimeter) of the samples were calculated using MATLAB algorithms. The pixelated units were converted into SI units (mm). The predicted values of the parameters were compared with the actual values. The goodness of fit was assessed using the coefficient of determination (R2), which was found to be 0.92, 0.88, 0.95, and 0.97 for the major diameter, minor diameter, surface area, and perimeter of the samples, respectively. In terms of mean absolute percentage error (MAPE), the accuracy of the model was found to be 95.64%, 94.74%, 95.32%, and 97.81% for the predicting major diameter, minor diameter, surface area, and perimeters of cardamom capsules, respectively. These results indicate that mobile images could be successfully incorporated in machine vision technology for the effective grading of cardamom capsules. [J Bangladesh Agril Univ 2021; 19(3.000): 398-405]