Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
A more efficient food production system is essential in all industries, but notably agriculture, to meet the needs of world's growing populace. However, there will be times when supply and demand are out of sync. One of the most difficult and time-consuming tasks in increasing agricultural output is managing and maintaining human and financial resources. In terms of increasing food production, managing resources, and manpower, smart agriculture is the way to go. to develop an IoT system for identifying crop diseases at a finer grain size by combining IoT with deep learning. This technology has the capability to identify agricultural diseases autonomously and provide farmers with diagnostic data. The research suggests a model for fine-grained disease diagnosis in the system called an attention-based convolution neural network with bidirectional long short-term memory (ACNN-BLSTM). The suggested approach incorporates a compensation layer that use a compensation algorithm to combine the outcomes of multidimensional recognition. It does this by first identifying in three dimensions: species, coarse-grained disease, besides fine-grained disease. The ACNN-BLSTM model's hyperparameters are fine-tuned using a hybrid approach called SA-GSO, which combines simulated annealing with glowworm swarm optimisation. This improves the model's detection performance. In comparison to other well-known deep learning representations, the studies demonstrate that the suggested neural network outperforms them in terms of recognition effect and usefulness for teaching real-world agricultural production tasks.
Keywords
Internet Of Things; Attention-Based Convolution Neural Network; Glowworm Swarm Optimization, Simulated Annealing, Agriculture, Crop Disease.
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Cuddapah Anitha
Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India.
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Cite this article
Cuddapah Anitha, Ambika B, Vasuki P, Rajesh Kumar T, Ebinezer M J D and Sheeba Santhosh, “Enhancing Agricultural Productivity: IoT and Attention-Based CNN-BLSTM for Fine-Grained Crop Disease Detection”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505020.
