AgriDefend is a $205 consumer‑grade edge device that fuses canopy imagery and soil probe data to detect crop stress before visible symptoms appear. It uses a MobileNetV2 CNN for image analysis, a Random Forest for soil trend features, and a logistic‑regression meta‑learner to produce a five‑class crop‑health prediction, all running on a Raspberry Pi 5 without cloud connectivity. In a greenhouse drought trial with wild radish, the fused model achieved 87 % accuracy and its soil signals predicted wilting about four days earlier than visual signs.
By George Xu, Nikhil Vincent, Elizabeth S. Forbes
arXiv:2609.10469v1 Announce Type: new
Abstract: Automated plant disease diagnosis is increasingly deployed on farmer-held devices in regions where agronomic expertise is scarce and network connectivi...
By Md. Abdullah Mandal, Saad Ahmed, Md. Khalid Syfullah
The paper introduces a dataset‑centric benchmark for deep learning approaches to grape leaf disease classification and detection. It evaluates publicly available datasets on disease categories, annotations, acquisition conditions, and class distributions, and tests representative models across image‑level classification, region‑level classification, and object detection. Results reveal high accuracy on controlled datasets but significant performance drops on heterogeneous, real‑world data, especially in cross‑dataset transfer and object detection tasks.
By Petar Canoski, Vlatko Spasev, Ivica Dimitrovski, Ivan Kitanovski, Petre Lameski
arXiv:2606. 02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management.
By Adri\'an C\'anovas-Rodriguez, Miguel A. Gonz\'alez-Ill\'an, Maria Fernanda Garc\'ia-Cruz, Pedro Nortes Tortosa, Jos\'e Salvador Rubio-Asensio, Miguel A. Zamora Izquierdo, Juan Antonio Mart\'inez Navarro, Antonio F. Skarmeta
FedXCrop is a federated learning pipeline for crop disease classification that preserves farmer privacy while providing post‑aggregation explainability. The system uses a MobileNetV2 model and achieves near‑centralized accuracy (98.81%–99.38%) across five clients, even under non‑IID label skew. Grad‑CAM and SmoothGrad explanations are quantitatively evaluated, showing that federated models largely retain explainability characteristics of centralized models, though agreement decreases with increased heterogeneity.
By Kriti Saini, Manikandan P
arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.
By Raunak Kumar, Soumyashree Kar