A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

In this study, a poultry egg incubator was designed, fabricated, and tested to evaluate its hatching performance. The incubator consists of a microcontroller with egg turner trays and incubating chamber of 116 nos. of egg capacity. The hatchability of the developed incubator was 79.3% and 87.1% hatchability during manual and automatic trials respectively. The temperature in the developed incubator was within the acceptable incubation temperature ranges from 37.6 °C to 38.6 °C. The average relative humidity in the developed incubator was maintained at 63.6% at manual and 55-65% at automatic trial. The eggs were turned manually approximately at 6 hours of interval. On the other hand, in the automatically controlled trial, it was done by egg turner maintaining exactly 6 hours of interval. It is noted that the percentage of hatching in rice husk incubators is below 55% which is much below comparing with the developed incubator. Also, the newborn chickens in rice husk incubators are unhealthy as they don’t get a sufficient amount of heat. Besides, in the sand incubation technique, kerosene-based hurricane lamps are used which produce Carbon Dioxide. The developed incubator is environment friendly because it doesn’t produce any by-product that is responsible for harming the environment. Also, after the successful trials, we have found the benefit-cost ratio was 1.42 which was quite satisfactory. The egg incubator can maintain the optimum conditions for the hatching of the chicken eggs and is capable of incubating and hatching the chicken eggs effectively. If the developed incubator is commercially supplied to the end-user, it will be a beneficial process of hatching for the farmer of Bangladesh.

Removal of hydrogen sulfide (H2S) from raw biogas is essential for feeding the refined gas with high methane (CH4) content in engines and combustion units. In small-scale farmhouses and power plants, a cheap, inexpensive, and facile desulfurization process is needed. Herein, the desulfurization process of biogas by treating with Fe2O3 and nanoscaled Ferrosoferric oxide (FeF) suspension is presented. The treatment process includes both the chemical reaction and physical adsorption phenomena. With variable pH and treatment duration, efficient desulfurization of the biogas was achieved. The optimum pH for H2S removal was 5 at which the removal efficiency was 97% for Fe2O3 solution and 95% for FeF solution. Respectively, CH4 content increased above 75%. The H2S-concentration dropped by 50 ppm from the initial value. The H2S content in the purified gas is thus reduced below the recommended limit for running internal combustion engines. We suggest some physicochemical elimination of the H2S fraction. The process might be feasible to utilize in small farmhouses and power units.

Biogas is the best renewable energy as it can be produced from any biomass for example any plant or living organism. The purpose of this research was to produce biomethane from co-digestion of vegetable and fruit waste with rumen digesta through anaerobic digestion process. In this research, two trials of experiment were conducted. Each trial has three different sample with different mixing ratios. Raw materials used in the experiment was rumen digesta of goat and cow, potato, capsicum, cucumbers, onions, radish, cauliflower, carrot, leafy vegetables, apple, banana, and papaya. In each sample, 1200 gram of raw materials were used. Hydraulic retention time was 30 days. Data was collected by water displacement method. The experiment found that the gas production started from 2nd or 3rd days and stops in 28th or 29th day. Highest production of biogas was 35, 33, 30, 40, 50 and 35 mL/day on the 17th, 14th, 17th, 11th, 12th and 7th day at the mixing ratios of 1:1:2, 1:2:1, 1:1.5:1.5, 1:0.5:0.5, 1:2:2 and 1.5:1.5:1 (Rumen Digesta: Vegetable Waste: Fruit Waste) respectively. The study suggests making digester for the recycling of waste to produce biogas, a renewable and environment friendly energy.

The demand for electrical power is rapidly increasing due to the rise of industries in developing countries. Power generation stations are having troubles to strike a balance between demand and generation. In this situation, it is urged that appropriate remedial action be taken. Rising power demand can be met by designing an efficient electric power generation system which will also help lowering the generation cost. It is shown that while high rated electric power generators are connected in parallel the value of neutral current is rising and the cooling temperature is also increased. Here, the goal of this experimental work is to present a new model for designing an efficient power production system for average-load (ranging up to 8000 Amp, 440 V) industries to minimize the demand on centralized interconnected grid. A scheme is proposed with four generators (2500 kVA, 2000 kVA, 2000 kVA and 1250 KVA) in parallel and enough cooling arrangement is provided with minimal cost. The coolant temperature is maintained 61 °C to 61.5 °C and at that time diesel temperature is not more than 38.5 °C. The amount of neutral-current is also optimized (up to 8.5 Amp.) which was more than 12 Amp. At the morning and afternoon, the neutral current is almost constant, but it is bit fluctuating between 7.5 Amp to 8.2 Amp at mid-day. The final outcome shows, the suggested system is efficiently stable with the change of load and generates optimal electricity.