Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach.

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models' learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed.

Triple-zero tillage and system intensification lead to enhanced productivity, micronutrient biofortification and moisture-stress tolerance ability in chickpea in a pearlmillet-chickpea cropping system of semi-arid climate.

Pearlmillet-chickpea cropping system (PCCS) is emerging as an important sequence in semi-arid regions of south-Asia owing to less water-requirement. However, chickpea (dry-season crop) faces comparatively acute soil moisture-deficit over pearlmillet (wet-season crop), limiting overall sustainability of PCCS. Hence, moisture-management (specifically in chickpea) and system intensification is highly essential for sustaining the PCCS in holistic manner.