2026· ITM Web of Conferences· Vol 90, pp. 03011· 0 citations· 19 references
TL;DR
Using a machine learning architecture that integrates soil moisture dynamics, meteorological causes, and management approaches, the yields of tomatoes and turmeric are anticipated using a hybrid stacking model that included en- semble and LSTM.
Abstract
Horticultural crop production prediction prior to harvest is challenging due to a lack of ground-based data. Utilising a machine learning architecture that integrates soil moisture dynamics, meteorological causes, and management approaches, the yields of tomatoes and turmeric are anticipated. A thousand samples were gathered over the course of three growth seasons in southern Karnataka using industrial-grade sensors. Four baseline models—Random Forest, XGBoost, Extra Trees, and LSTM— were evaluated using time-series cross-validation. For tomato and turmeric, XGBoost achieved the best baseline performance (R
2
=0.79, RMSE=3.94t/ha and R
2
=0.84, RMSE=1.31t/ha, respectively). For tomatoes (R
2
=0.85, RMSE=3.41t/ha) and turmeric (R
2
=0.88, RMSE=1.09t/ha), the best results were obtained by a hybrid stacking model that included en- semble and LSTM. SHAP identified soil moisture variability and rainfall timing as the primary culprits. The 77 farmers' feedback showed 8.
The proposed method performed better than the conventional LSTM algorithm in all forecasting scenarios and showed robust performance even at a 7-day forecasting lead time, showing promise for applications in short-range soil moisture prediction and environmental monitoring studies.
S. Samadianfard, E. Khajeh, Neda Beirami et al.· Applied Water Science· 0 citations
Three machine learning models Linear Regression, Random Forest, and XGBoost are developed and compared for predicting crop yield using the FAO global agricultural dataset to confirm that ensemble learning methods, particularly Random Forest, are well-suited for modelling complex agricultural yield patterns at global sc...
Abdulhafedh Alsarfi, A. Sable, Aymen M. Al-Hejri· International Journal For Mu...· 0 citations
This framework provides a reliable benchmark for leakage-controlled agricultural machine learning research rather than direct farm-level operational forecasting and demonstrates that engineered agroclimatic features can significantly improve country-level crop yield prediction, but model performance depends strongly on...
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Accurate predicting of crop yield is of greatest importance to the methods of precision agriculture and planning of food security. This study suggests a novel Multi-Modal Attention-Based Hybrid Deep Network (MAHDN) by combining heterogeneous data of agriculture, such as soil properties, weather parameters, and cropmana...
Ashwini V. Garole, Neha Jain, A. Pawar· International Conference on...· 0 citations
Accurate maize yield prediction is essential for ensuring food security and supporting agricultural planning in Kenya. However, the changes in climate and severe weather are posing more challenges to the stability of yield and food security. While advanced machine learning models, such as Long Short–Term Memory (LSTM)...
Stephen Gitau Ndung'u, Consolata Gakii· Journal of Global Innovation...· 0 citations
The increasing availability of agricultural timeseries data enabled more accurate and data-driven crop yield prediction. However, raw meteorological, soil, and vegetation datasets often fail to capture complex temporal dependencies essential for robust forecasting. This study proposes a structured feature engineering...
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