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Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Rice stands as one of the most water-intensive crops in agriculture, demanding a substantial volume of water annually for cultivation in Bangladesh. To address this considerable water requirement, the International Rice Research Institute (IRRI) introduced the alternate wetting and drying (AWD) irrigation method. This approach necessitates consistent manual oversight to maintain the desired water levels in the field. In pursuit of reducing this manual effort, a study was conducted at Bangabandhu Sheikh Mujibur Rahman Agricultural University. The study aimed to develop an automated version of the AWD irrigation system, assess its impact on plant growth and yield-contributing factors, and investigate water use efficiency through using that automated system. The automated AWD system incorporated water level controllers, solenoid valves, and water level detection probes to regulate water levels in the paddy field. The experiment followed a Randomized Complete Block Design (RCBD) with three treatments: T1 (Conventional Irrigation - CI), T2 (AWD), and T3 (Automated Alternate Wetting and Drying - AAWD), each replicated four times. The findings indicated minimal variation in growth and development parameters of the rice plants between the CI and AWD treatments. Likewise, yield components demonstrated no significant differences among the various treatments. Notably, the treatment with automated alternate wetting and drying (AAWD), namely T3, displayed the highest water use efficiency (WUE) at 65.20 kg/ha/cm, while the lowest WUE of 52.56 kg/ha/cm was observed in the conventional irrigation (CI) treatment T1. The results underscore the clear advantage of adopting an automated AWD system, yielding a 20% reduction in irrigation water usage compared to the conventional approach. This transition also minimizes the need for labor-intensive interventions in the paddy fields of Bangladesh, illustrating the potential for more efficient and sustainable rice cultivation practices.

Grading starfruit samples based on the stages of ripeness can be facilitated by machine vision system (MVS) that requires high-quality images. The evolution of modern smartphone cameras can be of assistance in this regard. This study was carried out to examine if smartphone image-based processing can be applied to categorize starfruit samples based on their maturity. In this regard, images of starfruit samples at three different maturity stages were acquired through smartphone camera. The MATLAB platform was used to extract the color features of the images. Each channel of the three-color space model (RGB, HSV, L*a*b*) was extracted. Principal component analysis (PCA) was applied to quantify the existence of variance in three different maturity classes. In addition, classification models were created using Linear Discriminant Analysis (LDA), Linear Support Vector Machines (SVM), Quadratic SVM, Fine K-Nearest Neighbor (KNN), and Subspace Discriminant Analysis (SDA). The best classifier was found to be the Linear Discriminant Analysis (LDA) which had an accuracy of 96.2% for calibration and 93.3% for validation. Accurate classification of the ripeness indices of starfruit samples demonstrated by LDA indicates the potential for commercial application of this technology.

Hydroponic fodder production in controlled environment (CE) settings have gained more focus in recent years due to the shortage of agricultural land for food production and the adverse effect of climate changes. However, the operation costs and dry matter issues are the major concerns for the sustainability of fodder production in the CE. This study provides a comprehensive literature review on techniques and control strategies for indoor environments and watering that are currently used and could be adopted in the future to achieve the economic and environmental sustainability of controlled environment fodder production (CEFP). The literature indicates fodder production in the modular system is becoming popular in developed countries, and low-tech systems like greenhouse are more prevalent in developing countries. The optimum temperature and RH range between 16-27°C and 70-80% to get efficient biomass yield; however, minimal research has been conducted to optimize the indoor temperature and relative humidity (RH) for efficient and higher efficiency fodder production. Besides, the water-saving techniques and optimal lighting spectrum need to be studied extensively. Automating and monitoring in CEFP system could reduce operating costs and improve quality and yield. Overall, this industry might have great potential for livestock production. Still, more strong research needs to be conducted to answer nutritional concerns and reduce the capital and operating costs for CEFP.

Mechanized farming is an indispensable part of modern agriculture. Bangladesh has managed to achieve significant progress in some components of farm mechanization i.e., irrigation and tillage. The majority of the agricultural operations are still being carried out with manual labor. This paper reports the status of agricultural machineries in the country while analyzing the historical policy changes towards intensive farm mechanization. The potential sectors requiring urgent attention for mechanization was also identified in this study. The manuscript discusses how mechanization could help ensure the food security of the country by maintaining timeliness of agricultural operation. Urgent introduction of mini-combined harvesters during COVID-19 pandemic was discussed as a commendable decision taken by the Government of Bangladesh. It also shows how the use of machine power is increasing in the country through custom hiring based ownership models of the machineries. Local manufacturing of agricultural machineries and spare parts was championed by the fact that small scale industries have developed in this sector. In line with this, we argued that the local companies should focus on precision manufacturing in the coming days. The manuscript also urged the need to include rural youth for sustainable farm mechanization process.

Absolute heading is an important parameter for a robot combine harvester or a robot tracked combine harvester, especially while it is turning, but due to the rapid turning of robot combine harvesters, its inertial measurement unit gives a gyro measurement bias that causes heading drift. Our research goal is to estimate the absolute heading of robot combine harvesters by compensating this gyro measurement bias during non-linear turning maneuvers. A sensor fusion method like the extended Kalman filter combined with the tracked combine harvester dynamic model and sensor measurements was used to estimate the absolute heading of a robot combine harvester. Circular, sinusoidal and concave shapes were used to evaluate the estimated heading produced by the sensor fusion method. The results indicate that the estimated heading is better than measured heading which was calculated from the integration of yaw rate gyro measurements, and the root mean square errors (RMSEs) for estimated headings are smaller than the measured headings. In practics, the target of this paper is thus the estimation of a heading or absolute heading that is bias compensated, and can be further used to calculate the exact crop periphery for automatic path planning of robot combine harvesters.