AI-Powered Precision Agriculture: Enhancing Crop Yields with Smart Analytics

The identification and classification of crops play a crucial role in modern agriculture. The use of unmanned aerial vehicles (UAVs) has greatly transformed crop monitoring, moving from conventional image processing techniques to more sophisticated deep learning approaches. Convolutional Neural Networks (CNNs) have played a key role in driving this change. UAVs provide high-resolution imagery that enhances the accuracy of crop differentiation. The foundation of precision farming lies in accurately identifying crops, which is essential for maximizing agricultural productivity and estimating yield. Unlike previous studies that focused solely on single-crop recognition from UAV-captured RGB images, this research introduces a novel deep learning approach. The proposed Conjugated Dense CNN (CD-CNN) architecture enhances crop classification by integrating a newly developed activation function, SL-ReLU. This method ensures efficient feature extraction and data fusion, facilitating accurate multi-crop identification. The control system of the drone makes use of sensors which help in keeping away the pests in the agricultural field, monitoring the water level in the field, checking the health of the crops using optical crop sensors [1]. By incorporating a dense block architecture, the risk of gradient explosion and loss of crucial features is minimized. Additionally, the use of a conversion block enhances processing efficiency. Unlike traditional CNNs, which rely on fully connected layers, the CD-CNN approach reduces feature loss and enhances classification accuracy.