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Pectin, a polysaccharide, is widely used as a gelling and thickening agent in the food industry. This study undertook optimization of pectin extraction from the peels of Baccaurea ramiflora Lour. (Burmese grape or lotkon), an abundantly grown wild fruit in Bangladesh. We applied the Box–Behnken Design of Response Surface Methodology with varied processing parameters including pH, time, and temperature. The Response Surface Methodology, employing a second-order polynomial model, successfully optimized the extraction conditions for the maximum pectin yield. The model predicted a pectin yield of 10.73%, which closely matched the experimental yield of 10.56%. The optimal conditions for pectin extraction were determined as pH (2.4), extraction time (56 min), and temperature (76°C). Further analyses of the extracted pectin under optimized conditions confirmed its excellent potential for food applications. The pectin was characterized by its moisture content (10.42%), water activity (0.51), ash content (3.41%), equivalent weight (769.23 mg/mole), methoxyl content (7.75%), anhydrouronic acid content (66.88%), degree of esterification (65.79%), and acetyl value (0.39%). These determined parameters strongly support that the pectin extracted from the peels of Burmese grapes is of good quality.

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.

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.

Adequate supply of micronutrients is important for the proper growth and yield of lentil, particularly in poorly fertile soil. This study was carried out to understand the effects of zinc (Zn), boron (B), and molybdenum (Mo) on the growth and yield of lentil, and how these elements can help manage soil fertility issues. In this regard, the morpho-physiological traits of lentils (BARI Masur-7) were collected from two experiments receiving the same treatments carried out during consecutive rabi seasons of 2015–2016 and 2016–2017. The experiments were laid out with a randomized complete block design having eight treatments, and was replicated thrice. The treatments were T1 (Control), T2 (Zn2.0 kg ha−1), T3 (B1.5 kg ha−1), T4 (Mo1.0 kg ha−1), T5 (Zn2.0B1.5 kg ha−1), T6 (Zn2.0Mo1.0 kg ha−1), T7 (B1.5Mo1.0 kg ha−1), and T8 (Zn2.0B1.5Mo1.0 kg ha−1). The results revealed that the application of micronutrients either singly or in combination had significant effects on the plant height, number of branches per plant, number of pods per plant, number of seeds per pod, thousand seed weight, and the seed yield of lentil. The maximum seed production was, however, observed in plots receiving treatment T8, i.e., the combined application of Zn, B, and Mo. Agronomic biofortification also had significantly increased protein content of lentil seeds while affecting the macro and micronutrient content of lentil seed. These results suggest that any micronutrient deficiencies might lead to a yield loss of lentil, and such a scenario could be avoided by a combined application of micronutrients at a proportionate level.