Leaf diseases are a major contributor to food and agricultural insecurity globally. The primary means of managing leaf disease through successful detection and classification of leaf diseases from field images. In this paper, we present a framework to automatically detect and classify plant leaf diseases, using a DCNN...
Anurag Gupta, T. Sardar, Sonali Bairagi et al.· International Conference on...· 0 citations
This study proposes AgriPredict–LoRaWAN Network (APLNet), an Internet of Things (IoT)-based agricultural monitoring and disease classification framework designed for cucumber farming that supports timely decision-making, improves productivity, reduces crop losses, and promotes sustainable cucumber cultivation.
P. Theerthagiri, A. Ruby, J. C. Chandran et al.· Scientific Reports· 0 citations
This research introduced a comprehensive multi-spectral imaging and machine learning framework that can easily revolutionize the disease diagnosis and management inside the low-resource farming communities while supporting scalability with actionable insights to empower farmers to mitigate crop losses and augment food...
J. Sastry, Pannangi Naresh, A. Ayesha et al.· Scientific Reports· 0 citations
An AI‐assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra‐reliable low‐latency Internet of Medical Things applications and a multi agent—deep deterministic policy gradient (MA‐DDPG) agent that allocates resources in a distributed manner and a hiera...
T. Sardar, Gousia Thahniyath, Ahlam I. Almusharraf et al.· Transactions on Emerging Tel...· 0 citations
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