S²AM: a sustainable smart agriculture model for crop protection based on deep learning

Agriculture is the golden thread that fastens all the sustainable development goals globally. However, the massive population explosion and ecosystem degradation have pressurized various auxiliaries of agriculture, primarily food security, crop protection, and disease identification. Although the penetration of digital technologies brings new opportunities to modern agriculture, the environmental facet has been neglected. Given this, the potential of sustainable computing and deep learning is investigated to handle critical agricultural technology impediments, lower resource expenditure, and propel sustainable agrarian developments. This research analyzes the relationship between Smart Agriculture and Sustainable Computing to balance the three pillars of Sustainable Agriculture practices—socio-economic–environment. Motivated by the analysis, the proposed work presents a deep learning-based lightweight, computation-efficient, performance-optimized, and explainable crop protection model to classify plant diseases. The proposed model reports accuracy, precision, recall, and F1-score of 99.4%, 99.4%, 99.5%, and 99.6%, respectively, outperforming state-of-the-art models. Further, the F1-score is improved by 15%, using 6.29 × fewer trainable parameters and 1.88 × fewer FLOPs that facilitate seamless deployment of the model on embedded devices, particularly for automated in situ plant disease classification. Moreover, to confirm the applicability of the proposed model across various crops, validation is conducted on additional crops, showcasing the model’s efficacy. The proposed model serves as a sustainable and innovative technological solution, aiding in the preservation of agricultural yields, enhancement of quality, and reduction of pesticide usage to safeguard the environment, achieved through energy-efficient resource utilization.