A Deep Learning Framework for Crop Yield Prediction in Precision Agriculture

Crop yield projections is quite significant in the present-day agricultural sector because it aids in proper resource
management, food security arrangement and sustainable agriculture. The traditional predictive models are usually
faced with challenges in handling nonlinear and complex agricultural data that is dictated by weather conditions, soil
type, plant species, pests activity, and agriculture. The area that has been examined in this paper is the hybrid methods
that have merged both machine-learning and neural-network methods to facilitate the accuracy and credibility of the
crop-yield forecasts. A set of multi-spectral satellite images 10,000 in total was used by them and the images captured
the states of vegetation during different levels of growth. Anisotropic Diffusion Filtering and Contrast Limited Adaptive
Histogram Equalization were used to pre-process the images to remove noise and emphasize important characteristics
in the crop. The Enhanced Fast Fuzzy C-Means (EFCM) algorithm was used to divide the crop regions and Multiscale
Gray-Level Co-Occurrence Matrix (MGLCM) was used to extract the texture features. Instead, Enhanced Convolutional
Neural Network (ECNN) was applied in yield prediction through deep residual connection to predict finer spatial
patterns, yet highly robust to noise and different illumination conditions. The hybrid model that is proposed performed
better than the traditional models such as RNN, DBN, and BPNN with a total accuracy of 97%. Its results were
compared in terms of such measures as accuracy, precision, recall, and F1-score. The findings prove that the
combination of preprocessing, feature extraction, segmentation, and state-of-the-art deep-learning systems can
increase the reliability of the predictions by a significant margin. The paper identifies opportunities of hybrid methods as
feasible solution to precision agriculture to allow farmers and researchers to make sound decisions, manage crops, and
enhance the productivit..