用AI自动识别椰子树病害，准确率达99.5%。
DeepSeqCoco: A Robust Mobile Friendly Deep Learning Model for Detection of Diseases in Cocos nucifera
- 基于深度学习的序列模型，融合SGD与Adam优化器提升性能。
- 相比现有模型准确率高5%，训练时间减少18%，预测快85%。
- 适合农业开发者、农民及智能植保系统部署使用。
椰子树病害严重威胁农业产量，尤其在发展中国家，传统农法限制了早期诊断与干预。当前病害识别依赖人工，效率低且难扩展。为此，我们提出DeepSeqCoco，一种基于深度学习的椰子树图像病害自动识别模型。在多种优化器设置（SGD、Adam及混合配置）下测试，以平衡准确率、损失最小化与计算成本。实验结果表明，DeepSeqCoco可达到高达99.5%的准确率（比现有模型高出最多5%），混合优化器配置实现最低验证损失2.81%。同时，训练时间最多缩短18%，输入图像预测时间最多降低85%。结果表明，该模型有望通过基于AI的可扩展、高效病害监测系统，推动精准农业发展。
原文摘要 · Abstract (English)
Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification methods are manual, labor-intensive, and non-scalable. In response to these limitations, we come up with DeepSeqCoco, a deep learning based model for accurate and automatic disease identification from coconut tree images. The model was tested under various optimizer settings, such as SGD, Adam, and hybrid configurations, to identify the optimal balance between accuracy, minimization of loss, and computational cost. Results from experiments indicate that DeepSeqCoco can achieve as much as 99.5% accuracy (achieving up to 5% higher accuracy than existing models) with the hybrid SGD-Adam showing the lowest validation loss of 2.81%. It also shows a drop of up to 18% in training time and up to 85% in prediction time for input images. The results point out the promise of the model to improve precision agriculture through an AI-based, scalable, and efficient disease monitoring system.
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