{"id":"W4312751102","doi":"10.1109/icccnt54827.2022.9984407","title":"Indian Crop Yield Prediction using LSTM Deep Learning Networks","year":2022,"lang":"en","type":"article","venue":"2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Yield (engineering); Crop yield; Agricultural engineering; Agriculture; Crop; Crop cultivation; Computer science; Work (physics); Machine learning; Soil fertility; Artificial intelligence; Environmental science; Agronomy; Soil science; Soil water; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002299369,0.0006183506,0.0002754466,0.0005781758,0.000189497,0.0005004863,0.0005665491,0.0003972057,0.001919506],"category_scores_gemma":[0.0007260806,0.0002022833,0.0004585917,0.0007903961,0.0001243689,0.000532681,0.0003248546,0.0006129579,0.0005188381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009417622,"about_ca_system_score_gemma":0.0004505652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02403712,"about_ca_topic_score_gemma":0.03209061,"domain_scores_codex":[0.9999205,0.000007344377,0.000004698873,0.00002754424,0.00001421994,0.00002578142],"domain_scores_gemma":[0.9997945,0.00006436629,0.00002577758,0.00001395348,0.00008640575,0.00001495896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003521193,0.0003317862,0.02517254,0.0001132852,0.0001650717,0.0003073443,0.00004533462,0.7741479,0.008444866,0.001003769,0.007054739,0.1828613],"study_design_scores_gemma":[0.000002790019,0.00001695714,0.002742876,0.000003528812,0.000009199704,0.000008612637,0.000006951139,0.9955478,0.0009676321,0.000429668,0.0002598293,0.000004218303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8499825,0.001085205,0.1296839,0.0008711392,0.000204042,0.00003942342,0.004272596,0.003323145,0.01053809],"genre_scores_gemma":[0.9841185,0.0002536353,0.009950737,0.00005878064,0.0000237217,0.00001844401,0.001822152,0.00003429795,0.003719747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02403712,"threshold_uncertainty_score":0.0477944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04835015335111407,"score_gpt":0.2500247670208082,"score_spread":0.2016746136696941,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}