一个创新的人工神经网络模型用于智能作物预测,使用基于感觉网络的土壤数据
Shabana Ramzan1, Basharat Ali2, Ali Raza3
1Government Sadiq College Women University Bahawalpur, Bahawalpur, Pakistan.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一种基于人工神经网络 (ANN) 的作物预测系统 (CPS),以帮助农民选择最佳作物. 该系统达到99%的准确性,提高了作物产量和农民利.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 农业生产率对于经济增长至关重要,但由于基于环境和土壤因素的低最佳作物选择而受到阻碍.
- 准确的作物预测对于最大限度地提高产量和农民收入至关重要,需要考虑各种环境和土壤参数.
- 推系统 (RS) 和机器学习 (ML) 为提高农业决策提供了有希望的途径.
研究的目的:
- 开发一种基于人工神经网络 (ANN) 的创新作物预测系统 (CPS),以帮助农民选择最合适的作物.
- 利用基于传感器的土壤数据,包括,,,温度,湿度,pH值,降雨量,电导率和土壤质地,用于准确的作物预测.
- 通过机器学习技术和超参数优化来验证拟议的CPS,以提高可靠性.
主要方法:
- 使用人工神经网络 (ANN) 架构开发一个作物预测系统 (CPS).
- 收集和分析基于传感器的土壤数据,包括关键的环境和化学参数.
- 用Python实现,使用准确度,精度,回忆和F1分数进行性能评估,包括超参数优化.
主要成果:
- 拟议的基于人工神经网络 (ANN) 的作物预测系统 (CPS) 在实时和基准数据集上实现了99%的高精度.
- 该系统有效地利用基于传感器的土壤数据和环境因素进行精确的作物建议.
- 超参数优化进一步完善了模型的学习方法,确保了强大的性能.
结论:
- 开发的作物预测系统 (CPS) 显著帮助农民做出明智的作物选择决定.
- 该系统的高精度转化为提高作物生产率和提高农民的利能力.
- 这种数据驱动的方法代表了精准农业和农业经济学的宝贵进步.
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