Environmental and Soil Parameters for Germination of Leaf Spot Disease in the Groundnut Plant Using IoT-Enabled Sensor System

Groundnut cultivation faces persistent challenges from leaf spot disease, which affects crop yield and quality. This letter investigates the influence of environmental and soil parameters on the germination of leaf spot disease in groundnut plants where the Internet of Things (IoT)-enabled sensor system is used to collect the data. A sensor array consisting of soil moisture, soil temperature, leaf wetness sensor (LWS), ambient temperature, and ambient humidity sensors are deployed to monitor critical environmental and soil factors. The aforementioned sensors collectively provide comprehensive insights into the conditions conducive to disease germination. Real-time data collection is achieved by deploying the IoT-enabled system, which collects the soil moisture, soil temperature, LWS, ambient temperature, and ambient humidity information and uploads them on the Thingspeak cloud. Field measurements indicates that the temperature (27 °C–32 °C), the length of LWS (0–10 h), high humidity (73% RH or more), soil moisture (4%–6%), and soil temperature (19 °C–20 °C) all have a significant impact on the germination of leaf spot disease infection. These results are incorporated into the creation of the multilayer perceptron (MLP) model, where accuracy of about 85.51% is achieved. The values of precision, recall, and F1-score are 87.54%, 84.21%, and 85.84%, respectively, achieved for the proposed MLP model. Further, the presence of the disease has been validated by collecting the images during the in-situ measurements. The findings enhance our understanding of disease dynamics and offer practical implications for precision agriculture and disease management strategies.