HYBRID DEEP LEARNING INTEGRATION ON ADVERSIAL NETWORK FOR AN ACCURATE AGRICULTURAL SEQUENCE DATA PREDICTION

In the Drip Irrigation system, Water scarcity and inefficient irrigation practices are major challenges that are faced in smart agriculture. To overcome these issues lot of Deep Learning (DL) methods are processed for prediction. Though there are lot of inefficiency and inaccurate in evaluation, an effective prediction is required in modern agriculture. To attain higher accuracy and robust prediction, this work presents an advanced sequence of Generative Adversarial Networks (SeqGAN) to generate and predict agricultural data that is specifically used to optimize irrigation practices and manage water resources effectively. The proposed seqGAN architecture consists of a generator and a discriminator. The generator involves a DL method of Long Short-Term Memory (LSTM) networks to create realistic agricultural text sequences by learning from previous data and it also has controlled variability through noise injection. The discriminator includes a Gated Recurrent Unit (GRU) and a CatBoost classifier to differentiate between real and generated sequences. The CatBoost integration enhances the model's ability to handle categorical data efficiently which enhances the accuracy and robustness of sequence classification. This proposed method is particularly beneficial for agricultural datasets augmenting and effective predictive sequences in agricultural fields. This proposed work not only improves data availability but also supports innovative solutions in agricultural research that ultimately contribute to more sustainable and efficient farming tasks than conventional methods.