<p>Estimation of crop growth cycle assessment via satellite imagery requires integration of multiple image processing. Existing assessment models have higher complexity and lower scalability, which limits applicability for hybrid image sets. Moreover, these models require large datasets, which increases delays needed for training and validation operations. Deal with the problems this proposed model of crop growth cycle assessment model via deep-learning &amp; bioinspired computing operations on satellite images. The model initially collects limited imagery sets from google earth engine, and augments them via band filtering operations. These augmented images are processed via estimation of VH (vertical to horizontal) i.e. cross polarization and VV (vertical to vertical) co-polarizations, which assists in estimation of green regions. The estimated regions are processed via a multimodal series of feature extraction models that includes Fourier, Cosine, Wavelet and Convolutional transforms. Extracted features are selected and computed through the highly variant features set by an Elephant Herding Optimization (EHO) algorithm. These sets are then classified into different growth cycles via a customized 1D Convolutional Neural Network (CNN) that assists to improve accuracy while maintaining low complexity levels. Due to the combination of such low complexity modules, the proposed model is capable of accurate classifications, with better accuracy &amp; measured performance when compared with models for crop health estimation techniques. The performance of the proposed model achieved 8.5% higher in terms of accuracy, 6.4%, higher precision, 5.9% better recall, and 4.5% lower speed when compared with standard deep learning methods, which makes it useful for a wide variety of satellite image processing applications.</p>