There has been an unprecedented uptake of single-axle tractors due to their low price point and ability to maneuver within the smallholder farming system. Unfortunately, this tractor exposes farmers to high hand-transmitted vibrations that have been found to predispose them to Hand-Arm Vibration Syndrome (HAVS), a serious work-related condition caused by damage to the vascular, neurological, and musculoskeletal systems. This study aimed to examine the efficacy of a rubber handle vibration damper in minimizing the vibration exposure of operators while handling single-axle tractors. Experimental investigations were carried out during operation in a field experiment. The vibration tests were carried out on a 15 hp single axle tractor in second and third gears. Accelerations were measured using RMS values in three orthogonal directions (vertical, forward, and lateral) before and after damper application. A (8) and HAVS latency period were also calculated. The results indicated an effective reduction in vibrations in all directions by 29%-50%. The average reductions were 42% in the second gear and 46% in the third gear for vibrations. Accordingly, significant reductions in the A (8) values were recorded despite some exposures being higher than those required for optimal safety. Significantly, the latent period for HAVS was extended by approximately 89%-90%, suggesting a considerable delay in developing vibration-related conditions. Conclusively, the rubber vibration dampener is very effective in reducing hand-transmitted vibrations and ensuring improved occupational safety. Nonetheless, further measures should be implemented in conjunction with vibration damping.

This study investigates genetic algorithm (GA)-based feature selection for soil quality classification in smart agriculture using a synthetic soil-climate dataset. Rather than employing GA as the classifier itself, the method uses GA to optimize the subset of predictors supplied to a class-balanced logistic regression model. Each chromosome encodes the inclusion or exclusion of 10 soil and climate variables, while the fitness function combines F1-score, balanced accuracy, and a sparsity bonus to reward compact solutions under class imbalance. With a population size of 24 and 20 generations, the evolutionary search identified Irrigation_Available and Soil_Nitrogen as the best two-feature subset. On the hold-out test set, the GA-selected model achieved an F1-score of 0.2934, nearly identical to the full-feature baseline (0.2941), while improving balanced accuracy from 0.7950 to 0.8159. These results indicate that GA-based feature selection can reduce model complexity substantially without materially degrading predictive performance. The findings support the use of evolutionary optimization as an interpretable decision-support layer for data-driven agricultural analytics.

Agricultural sustainability underpins global food security, yet optimizing fertilizer application remains a critical challenge. Under-application leads to yield loss, while over-application causes environmental pollution and economic waste. This study presents a Genetic Algorithm (GA)-based optimization framework that recommends a fertilizer blend for individual soil samples. Taking nitrogen (N), phosphorus (P), and potassium type crop variety as input, the system searches a seven-dimensional dosage space (kg/ha) spanning seven commercial fertilizers. The fitness function is grounded in Liebig’s Law of the Minimum, augmented with total-cost and nutrient-overshoot penalties to yield a multi-objective structure. The GA comprises BLX-α crossover, Gaussian mutation, tournament selection, and elitism operators. The system is validated on a dataset of 8,000 field records spanning eleven crop types and five soil categories. Case studies conducted on five representative crops demonstrate consistent convergence within 120 generations. In a batch analysis of 500 samples, the distribution of GA recommendations aligns with agronomic nutrient-deficiency patterns rather than with historical labels.

Efficient irrigation scheduling is a central challenge in precision agriculture because crop water demand varies with soil properties, climate conditions, growth stage, season, irrigation method, water source, and regional characteristics. This study presents a genetic algorithm-based smart irrigation optimization model that produces operational irrigation recommendations rather than only classifying irrigation need. The model uses an irrigation data set containing 10,000 complete records and 20 variables grouped into soil conditions, weather conditions, agricultural information, and irrigation history. Each candidate irrigation plan is represented as an eight-gene chromosome covering irrigation amount, frequency, time, duration, irrigation type, water source, mulching status, and regional adjustment coefficient. A compound fitness function evaluates candidate plans by combining water adequacy, timing suitability, frequency suitability, duration consistency, method efficiency, source suitability, mulching contribution, regional calibration, soil quality, and excessive water penalty. The evolutionary process uses tournament selection, two-point crossover, adaptive mutation, and elitism. Experimental results show that the population improves across generations and converges toward an interpretable irrigation schedule. The best plan reached a fitness value of 830.2539 in generation 149 and recommended 10.77 mm of water every five days at 18:00 for 14.83 minutes using drip irrigation, rainwater, mulching, and a regional coefficient of 0.91. The results indicate that genetic algorithms can provide a transparent and flexible decision-support structure for smart irrigation planning when multiple agronomic and operational variables must be considered together.