Agricultural Field Zoning and Crop Optimization Using a Genetic Algorithm
Release :
2026/09/01
Language :
en
Year :
2026
Issue :
Vol. 1 / Issue 1
Pages :
1-7
Download Count
58
Abstract:
Sustainable crop planning requires matching local soil and climate conditions with crop requirements, especially when a single agricultural field contains zones with different nutrient, moisture, temperature, acidity, and rainfall profiles. This study presents a genetic algorithm-based decision support model for agricultural field zoning and crop optimization. The model divides one field into four zones and recommends the most suitable crop for each zone using nitrogen, phosphorus, potassium, temperature, humidity, soil acidity, and rainfall measurements. A public crop recommendation data set containing 2,200 observations, eight attributes, and 22 balanced crop classes is used to define crop profiles. All numerical variables are normalized to a common interval, and the fitness function measures the compatibility between each zone and candidate crop through normalized distance-based similarity. Each chromosome contains four crop assignments, one for each field zone, and the evolutionary process uses tournament selection, single-point crossover, mutation, and elitism. The final solution recommends rice for Zone 1, black lentil for Zone 2, pigeon pea for Zone 3, and coconut for Zone 4. The total fitness value reaches 3.6060 out of 4.0000, corresponding to an overall efficiency of 90.15 percent. The results show that genetic algorithms can provide an interpretable and flexible framework for zone-specific crop allocation when multiple agronomic variables must be considered simultaneously.
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