Contrary to popular belief, agriculture is becoming more data-driven with artificial intelligence and Internet-of-Things (IoT) playing crucial roles. In this paper, the integrated processing executed by various sensors combined as an IoT pack and driving an intelligent agriculture management system designed for rainfall prediction and fruit health monitoring have been included. The proposed system based on an AI aided model makes use of a Convolutional Neural Network (CNN) with long short-term memory (LSTM) layer for rainfall prediction and a CNN with SoftMax layer along with a few deep learning pre-trained models for fruit health monitoring. Another model that works as a combined rainfall predictor and fruit health recognizer is designed using a CNN + LSTM and a multi-head self-attention mechanism which proves to be effective. The entire system is cloud resident and available for use through an application.

与普遍看法相反，农业正变得更加数据驱动，人工智能和物联网 (IoT) 发挥着至关重要的作用。在本文中，包括由各种传感器组合成物联网包执行的集成处理，并驱动专为降雨预测和水果健康监测而设计的智能农业管理系统。所提出的基于人工智能辅助模型的系统利用具有长短期记忆 (LSTM) 层的卷积神经网络 (CNN) 进行降雨预测，并使用具有 SoftMax 层的 CNN 以及一些针对水果的深度学习预训练模型健康监测。另一个结合降雨预测器和水果健康识别器的模型是使用 CNN + LSTM 和多头自注意力机制设计的，事实证明该模型是有效的。整个系统驻留在云中，可通过应用程序使用。