Agricultural and Biological Engineering

A review on the trends and technologies in biomass composting

2025-08-10T18:52:44+07:00

Biomass resources abound in the Philippines, including agricultural crop residues, forest residues, animal waste, agro-industrial waste, urban solid waste, and aquatic garbage. The common agricultural wastes in the country are rice straws, rice husks, corn cobs, sugarcane debris, cacao waste, coconut shell, and coconut. Composting is the controlled aerobic biological breakdown of organic materials into a stable, humus-like product called compost. It's essentially the same process as natural decomposition, but it's accelerated and enhanced by mixing organic waste with other substances that promote microbial development. Composting has long been recognized as an effective method for recycling organic waste. However, despite its benefits, traditional composting still faces several limitations that hinder its wider adoption and efficiency. According to Ayilara, et al. [1], the major challenges include difficulty in detecting and controlling pathogens, inconsistent material quality, prolonged composting and mineralization periods, and issues related to odor generation. These constraints reduce overall productivity and limit the potential of composting as a scalable waste management solution. Chemical fertilizers contribute to greenhouse gas emissions, environmental degradation, the extinction of soil organisms, marine life, ozone layer depletion, and even human diseases. Composting agricultural wastes has been a regular technique among farmers in rural areas in recent years. With the inclusion of other materials, organic waste is degraded into organic fertilizer in a natural composting process. Compost is acknowledged as one technique to improve the soil's nutritional status by releasing accessible nutrients such as nitrogen and phosphorus from additional organic leftovers via microbial decomposition. Composting is an important part of agriculture since it promotes the recycling of farm waste. Due to the presence of materials that take longer to compost, particularly during co-composting, and a lack of proper composting technology, the protracted composting process and laborious hand mixing of the compost pile are definitely challenging. This review article examines how waste is managed through different composting methods, different elements that affect composting, the long composting processes and the theories behind them, compost bioreactor technologies, and current trends and future possibilities in composting. In addition, this review article also shows that the degradable organic components used in composts are evaluated for their capacity to mineralize slowly, making them beneficial to crops. As a result, the composting processes have been improved.

Rainfall intensity analysis for radar rainfall evaluation in the composite area of Takhli and Sattahip radar

2025-11-23T19:41:53+07:00

This study collected data on a total of 510 rain events between February 2018 and November 2020. It includes hourly rainfall data (R) from 238 ground-based automated telemetry stations and radar reflectivity data (Z) under a measurement radius of 240 km from Takhli and Sattahip radars. The Z-R relationships of Takhli radar and Sattahip radar were determined and applied to evaluate radar rainfall intensity in the composite area of these radars. The radar rainfall intensity in the composite area was analyzed using five methods: the rainfall intensity from (1) Z = 138R^1.6(only Takhli radar), (2) Z = 170R^1.6 (only Sattahip radar), and the composite rainfall intensity from (3) Z = 200R^1.6 (Marshall and Plamer), (4) Z = 300R^1.6 (Woodley and Herndon), and (5) Z = 138R^1.6 and Z = 170R^1.6 (Takhli and Sattahip radar). These results were compared with the ground-based station data to determine the best method for evaluation based on the least statistical values, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and BIAS. The results show that the composite results from Takhli and Sattahip radar were the most accurate, and this case can increase the accuracy compared to the 1^st to 4^th method by 22.26%, 10.25%, 3.89%, and 18.02% of the RMSE; 29.75%, 23.14%, 14.88%, and 14.88% of the MAE; and 361.54%, 42.31%, 100.00%, and 369.23% of the BIAS value, respectively.

Potential utilisation of Convolutional Neural Network (CNN)-based banana bunch ripeness classification to effectuate banana harvesting process

2025-11-20T12:19:12+07:00

The classification of banana bunch maturity represents a vital preliminary phase for maintaining fruit quality. However, prior studies related to non-destructive maturity classification have predominantly focused on ready-to-sell finger bananas despite the application of industrial-scale banana harvesting, which is done by bunches. This research aimed to categorize banana fruit bunches' ripeness status before the harvesting process. The classification process distinguishes between two maturity levels (unripe and ripe) utilizing the model comparison between Convolutional Neural Network (CNN), Visual Geometry Group (VGG) 16, and EfficientNet methodology. The dataset comprises 500 banana bunch images for labeling purposes. The data was partitioned in a 4:1 ratio for training and testing. The developed model utilizes CNN architecture that includes convolutional (Conv2D), pooling (MaxPooling2D), and fully connected layers. Evaluation outcomes indicate that the model effectively classifies the maturity of banana bunches, demonstrating high accuracy, precision, and recall. The conventional basic CNN resulted in the most optimal model among VGG16 and EfficientNet with precision up to 91.11%. This CNN-based classification system is anticipated to be integrated into the banana industry, aiming to maintain the harvested banana bunches. By employing CNN for classifying the maturity of banana bunches, the harvesting process can be made more efficient with less time needed. Furthermore, the system enhances automation and consistency in product quality while decreasing dependence on manual labor. Additionally, the classification outcomes can be directed towards appropriate processing pathways, thereby facilitating the implementation of smart technology-driven postharvest systems over time.