Early Detection of Diseases in Hydroponic Saffron Crops Using a Diffused Concurrent Convolution Neural Network for Smart Farming
The detection of diseases in hydroponically cultivated saffron should be carried out as early and accurately as possible to maintain the quality of the yield, minimize losses, and promote sustainable farming practices. Manual diagnosis strategies are not suitable for high-density hydroponic systems where early symptoms tend to be subtle, as these methods are slow and rely extensively on experts. This research aims to develop a novel framework based on deep learning technology, using a Diffused Concurrent Convolutional Neural Network (DCCNN) to perform image analysis and detect diseases in saffron crops. The modified DCCNN includes a hierarchical three-stage classification pipeline consisting of crop recognition, disease detection, and Classification of the specific diseases, adding an “unknown” category for non-target or ambiguous outputs at each stage to enhance flexibility. The digression from standard deep learning techniques is justified due to the DCCNN construction, which contains a learnable diffusion layer and concurrent multi-scale convolutional blocks, and thus encapsulates strong feature propagation with fine-grained detection of complex and low-data environments. Evaluation on a specially annotated dataset of hydroponic saffron showed strong performance with up to 99.4% classification accuracy, exceeding well-known CNN baselines including EfficientNet and ResNet50. Additionally, the model processes static crop images and associated environmental sensor data, collectively referred to as 'non-sequential crop data,' focusing on spatial features without temporal dependencies. These findings confirm that the system, which is based on DCCNN, provides a transferable solution for precision disease detection in controlled-environment agriculture systems and can be extended to other high-value crops.
Volume & Issue
Vol. Volume 20 / Iss. Issue 2