Fertilisers are essential to agriculture since they provide crucial nutrients that stimulate crop growth, increase the yield and improve the yield quality. However, this practice requires the use of manual labour, which consumes energy and time as well as limits the effectiveness of fertilisation methods and is environmentally harmful. These factors have led to the development of an innovative product based on the Internet of Things (IoT) called the Auto Sprinkler Rover, which is a remote-controlled machine that operates automatically. It contains a 12 L water barrel that serves as a storage tank for a liquid fertiliser. This study designed a liquid sensor and light emitting diode to alert th-e user to the amount of liquid in the tank. The water pump facilitates the smooth spraying of liquid fertiliser from the pipe's nozzle. The Auto Sprinkler Rover differs from conventional methods because it integrates real-time monitoring via Wi-Fi, an automatic liquid level sensor as well as an automatic spraying system and an ergonomic design that is manually operated. The Auto Sprinkler Rover was designed to be a cutting-edge technology that benefits sustainable crop cultivation for households, communities and the global agriculture industry.
Mechanical sugarcane harvesting generates substantial material losses that are associated with the equipment age. This study evaluated the relationship between the harvester service life and the operational efficiency by analysing field losses and product contamination across machines with varying operational histories (1, 14, 16, and 17 years) in Chaiyaphum Province, Thailand, using a randomised complete block design. The results indicate that the 17-year-old machines exhibited 54% higher total losses (241.93 kg.ha-1) compared to the newer equipment (156.90 kg.ha-1). The field losses were attributed primarily to base cutting operations (36%) and roller mechanisms (34%), collectively accounting for 70% of the total losses. The contamination analysis revealed sugarcane tops as the predominant impurity source (57% The revenue loss analysis indicates excessive field losses from ageing equipment reducing the farm profitability by 12-18%. The non-linear relationship between the equipment age and performance demonstrates that maintenance practices significantly influence degradation patterns, providing critical insights for optimising mechanical harvesting systems.
The non-destructive estimation of the corn kernel moisture content is essential for determining the optimal harvest period. Although various spectrometer sensors are currently available, their predictive performance differs due to variations in the spectral resolution and wavelength coverage. This study compared the performance of several portable spectrometer sensors with different wavelength ranges for predicting the corn moisture content. Spectral data and reference moisture content were used to develop the prediction models using partial least squares regression (PLSR) and an artificial neural network (ANN). Based on the PLSR modelling, the AS7265X and C12880MA sensors produced the best performance, with coefficients of determination (R2) for training and testing reaching up to 0.90. Furthermore, the ANN modelling yielded improved predictive accuracy, with the highest R2 value of 0.95 obtained using the same sensor combination. These results demonstrate that portable spectrometers show strong potential for the non-destructive field-based prediction of the corn moisture content and can serve as a reliable indicator for determining the optimal harvest timing.
The objective of this study was to evaluate the potential for the sustainable reuse and the value of the residual nut mixture (RNM) by-products (cashew nut, peanut, and soybean) after extraction. To investigate the drying kinetics, the RNM was dried at various temperatures (50 to 80 °C). The Balbay and Şahin model, which had a high coefficient of determination (R2) of 99.62-99.96%, a low root mean square error of 0.007-0.021, and χ2 of 0.001-0.005, was the one that best fit the experimental data out of the eight mathematical models that were used. Artificial neural networks showed higher and faster prediction capacity than the mathematical models. The effective moisture diffusion coefficient (Deff) increased gradually with the temperature, with an activation energy (Ea) of 13.67 kJ∙mol-1. The RNM powder produced by the optimal drying process (60 °C for 3.75 h) has a bright colour, high polyphenol content (2.68 mg gallic acid equivalent (GAE)∙g-1) and antioxidant activity, low moisture content (4.9%) and relatively high nutritional value, especially protein (27.27%), lipid (40.19%), and fibre (4.2%). Under these conditions, not only is efficient drying and preservation achieved, but the quality of the by-product powder is also maintained.
