スマート農業機器の軽量化モデルを用いた,インテリジェントな作物収穫量予測による,気候に耐性のある農業のためのオンデバイスAI
Rajesh Kumar Dhanaraj1, M Maragatharajan2, Aanjankumar Sureshkumar2
1Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India. sangeraje@gmail.com.
Scientific reports
|August 25, 2025
まとめ
この研究は,持続可能な農業のために水の利用を最適化するために,ランダムフォレスト (RF) を使用したインテリジェントの作物収穫予測システムを導入します. 軽量なAIモデルは90.1%の精度を達成し,水管理を強化し,気候に適応した農業を促進しました.
科学分野:
- 農業技術
- 農業における人工知能
- 持続可能な農業
背景:
- AI アプリケーションはますます農業用消費用電子機器に統合され,プロセスインテリジェンス,効率性,持続可能性が向上しています.
- 水の使用を最適化することは,持続可能な農業,気候の回復力,環境への影響の軽減に不可欠です.
研究 の 目的:
- 農業における水の利用を最適化するために,インテリジェントな作物収穫予測システムを開発する.
- 軽量な機械学習モデルと消費者電子機器を統合し,水管理を強化する.
- 効率的な灌スケジューリングを通じて持続可能な農業の実践を促進する.
主な方法:
- ランダムフォレスト (RF) 分類器を使用して,作物の収穫量予測と水の使用最適化を行いました.
- センサーやスマートディスプレイデバイスを含む消費電子機器と統合された軽量機械学習
- 持続可能性の予測のための最小限のメモリリソースでリアルタイム農業データでモデルを訓練しました.
主要な成果:
- 農地での作物収穫の適性を予測するのに90.1%の精度を達成しました.
- AI対応のIoT (89%),LoRaベースのシステム (87.2%),アダプティブAI (88%) などの既存の方法を上回った.
- クラウドに依存せずにリアルタイムで意思決定を行うための計算効率の高い機械学習モデルの有効性を実証しました.
結論:
- 提案されたインテリジェントシステムは,正確な作物収穫量予測を通じて,水の使用を効果的に最適化します.
- 消費電子機器と統合された軽量な機械学習モデルは 持続可能な農業のための実用的な解決策です
- このシステムは水管理を向上させ 環境への影響を軽減し 気候に適応した農業を 支援します
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