Zone-Aware Greenhouse Control and Multitask Crop Yield Prediction Using ConvLSTM and EMMYP-Net

K. P. Mayuri; Sheela Kathavate

doi:10.48084/etasr.15707

Authors

K. P. Mayuri Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Bengaluru, India, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India
Sheela Kathavate Department of Information Science and Engineering, BMS Institute of Technology and Management, Bengaluru, India, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India

Volume: 16 | Issue: 1 | Pages: 31874-31879 | February 2026 | https://doi.org/10.48084/etasr.15707

Received: 22 October 2025 | Revised: 14 November 2025, 30 November 2025, 3 December 2025, and 6 December 2025 | Accepted: 7 December 2025 | Online: 19 December 2025

Corresponding author: K. P. Mayuri

Abstract

In the new age of greenhouse management, smart space adaptive systems are required to allow environmental control and crop analytics. This study presents an end-to-end deep learning architecture for spatiotemporal prediction and control in agriculture, which enables seamless integration between the different components of a decision loop. A ConvLSTM model predicts zone-specific microclimate variations, and a Dueling DQN agent selects the optimal actions for irrigation, ventilation, and fertilization according to energy demand, emission prediction, and soil moisture balance. The proposed EMMYP-Net (Enhanced Multimodal Multitask Yield Prediction Network) involves a CNN-BiLSTM-attention architecture that combines visual canopy data with multi-sensor sequences to co-classify growth stages and estimate yield. Experimental tests in a 1,200 m² four-zone greenhouse showed remarkable improvements, as ConvLSTM decreased RMSE by 45±2.3% compared to ARIMA and by 31±1.8% compared to LSTM. EMMYP-Net achieved an accuracy of 96.0% for classification, as well as an R² of 0.912±0.007 in predicting yields. This process-integrated approach enhanced resource sustainability by achieving savings of 19.3% in energy, 16.5% in water, and 15.2% in fertilizers relative to a conventional system. Combining predictive control and crop intelligence offers a scalable basis for sustainable data-driven greenhouse management. The key novelty of this work lies in the seamless integration of ConvLSTM-based spatiotemporal forecasting with the EMMYP-Net multimodal crop analytics within a unified reinforcement-learning-driven decision loop, enabling both predictive control and biological feedback in real time.

Keywords:

background zone-aware greenhouse control, ConvLSTM, dueling deep Q-Network, EMMYP-Net, CNN-BiLSTM-attention, spatio-temporal forecasting, multitask learning String Precision agriculture, reinforcement learning, intelligent greenhouse system

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