Leveraging EfficientNetB3 for Automated Plant Stress Identification

Automated plant stress diagnosis using EfficientNetB3 architecture eliminates agricultural constraints of conventional manual inspection methods. This paper demonstrates that EfficientNetB3 can identify many stress situations including insect infestations, nutrition deficits, and drought. The model obtained an incredible 98.2% accuracy using a diverse collection of plant images using transfer learning. The results show the potential of the model to change plant health monitoring by offering a scalable and exact solution appropriate for real-time and limited resources settings. The remarkable accuracy achieved by EfficientNetB3 indicates its efficiency in early stress detection, hence reducing crop losses. Future research will focus on merging multimodal data and undertaking field-based testing to improve robustness and applicability, so promoting more sustainable and resilient farming practices.