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    • 56. 发明专利
    • DISEASE DETECTION USING IOT AND MACHINE LEARNING IN RICE CROPS
    • AU2020102100A4
    • 2020-10-22
    • AU2020102100
    • 2020-09-02
    • KHAN MOHD ARSH MRSHARMA NITESH KUMAR MRSINGH VINAY KUMAR DR
    • SHARMA NITESH KUMARKHAN MOHD ARSHSINGH VINAY KUMAR
    • G16Y40/10A01G22/22G06K9/20G06K9/62G06N20/00G06Q50/02G16Y40/35
    • DISEASE DETECTION USING IOT AND MACHINE LEARNING IN RICE CROPS Agricultural development is the vital form of income and a major part of an Indian economic growth is dependent on crop cultivation. In order to improve crop production and benefit, accurate diagnosis of plant leaf diseases is crucial. For several causes one of the main concerns for the aforementioned issue is crop disorders, farm workers are still suffering losses in growing crops. This is related to inadequate of information on the infection and the appropriate pesticides or insecticides to combat the infection. But identifying the actual illness and supplying the right treatment necessitates professional assistance or advanced experience to manage the illness. Farming professionals detect most diseases by analyzing visible indications. Nevertheless, producers have little exposure to the specialists. Rice grains often affected by Rice Blast and Bacterial Blight hence contributes to decline in rice crops productivity. This proposal integrates the machine learning and Internet of Things concepts for detecting the diseases in the rice plant. The IoT technology solutions facilitate remotely tracking of the agricultural sector's knowledge from the landscape. Temperature, air pressure, water level, level of sunshine is tracked and transmitted to the cloud. From residence the farm workers will track the plantation's sustainability records. A Machine Learning model that incorporates the Convolutionary Neural Network (CNN) algorithm to diagnose rice plant diseases leveraging the photos and include the necessary treatment. The treatments include accurate details concerning about the utilization of fertilizer or fungicide to treat the illness. 11 P a g e DISEASE DETECTION USING IOT AND MACHINE LEARNING IN RICE Diagram StlINGDAASE FIG:FLDIGRTA AND TFNa(1ge €1ASS1)uf(WIION ('3 4%N 11 TIN j4l N ALGORlfiMl Dis£5Es FIG 1: FLOW DIAGRAM 1 |P a g e