Bio-inspired and artificial intelligence enabled hydro-economic model for diversified agricultural management
نویسندگان
چکیده
Neoteric phenomena such as climate change, scarce water availability and excessive fertilizer usage necessitate an augmentation of resource utilisation efficiencies in the agricultural sector. There is a need to reorient agroecosystems curb stress on environmental resources while meeting rising socio-economic objectives under changing hydro-climatic conditions. Considering this, optimal land allocation for diversified agriculture essential. We propose combinatorial optimisation approach considering agronomic, socio-economic, using bio-inspired optimization algorithms. The stochastic tackles problem crops multidimensional context by simultaneously addressing conflicting goals farm-level risk management well district-level contingency planning. sensitivity proposed framework are assessed through case study Dharwad district Karnataka, India data (water consumption cost, crop type, cultivable land, man machine hours, etc.) from year 2019–2020. Results indicate that Multi-objective Genetic Algorithm (MOGA) more capable optimising suggesting Although Cuckoo Search (CS) Particle Optimisation (PSO) also produced productive Pareto fronts, they were observed be less effective than MOGA. annual increase profits yield obtained MOGA 103% 97% respectively, reduced 5% compared conventional routines Dharwad. hydro-agronomic decision support (DSF) can utilised assist AI-enabled planning process sustainable agroecosystems.
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ژورنال
عنوان ژورنال: Agricultural Water Management
سال: 2022
ISSN: ['0378-3774', '1873-2283']
DOI: https://doi.org/10.1016/j.agwat.2022.107638