| Method for integration of deep learning and internet of things based plant disease detection for smart agriculture, involves empowering farmers to make informed decisions regarding crop management, for resulting in better productivity, and resilience against diseases | |
| 2025-03-29 | |
| 专利权人 | GIRADKAR S S (GIRA-Individual) ; WANJARI S (WANJ-Individual) ; RAUT P H (RAUT-Individual) ; REDDY S C M (REDD-Individual) ; NAYAK K (NAYA-Individual) ; RAWAT S (RAWA-Individual) ; DEEPA D (DEEP-Individual) ; MARRI U (MARR-Individual) ; RVS P (RVSP-Individual) ; BATHALA S (BATH-Individual) ; SIVANATHAN M (SIVA-Individual) ; ALAUDDIN S (ALAU-Individual) |
| 申请日期 | 2025-03-29 |
| 专利号 | IN202511030630-A |
| 成果简介 | NOVELTY - The method involves utilizing internet of things (IoT) sensors for continuous remote monitoring of crop health, so that farmers receives real-time data regarding potential disease outbreaks, for allowing for immediate action that minimizes crop losses and optimizes yield. IoT and deep learning solutions are implemented to lead to significant cost savings through optimized resource use e.. targeted pesticide application, for reducing overall operational costs and increasing profitability for farmers. Sustainable farming is provided by minimizing chemical usage and supporting precision agriculture practices, to protect the environment and promote biodiversity. The farmers are empowered to make informed decisions regarding crop management, for resulting in better productivity, resilience against diseases, and ultimately contributing to improved food security. USE - Method for integration of deep learning and internet of things (IoT) based plant disease detection for smart agriculture for farmers. ADVANTAGE - The method enables eliminating remaining values in the image to eliminate the characteristics of leaf illnesses. The method allows the deep learning model to be trained on a wide range of plant photos, both healthy and diseased, to train the deep neural network model, that allows to identify complex patterns and characteristics linked to different plant illnesses using visual input from IoT devices, so that the model is able to reliably classify the health status of plants using visual inputs from the IoT devices. |
| IPC 分类号 | G06N-003/045 ; G06N-003/08 ; G06T-007/00 ; G06V-010/764 ; G06V-010/82 |
| 国家 | 印度 |
| 专业领域 | 信息技术 |
| 语种 | 英语 |
| 成果类型 | 专利 |
| 文献类型 | 科技成果 |
| 条目标识符 | http://119.78.100.226:8889/handle/3KE4DYBR/13356 |
| 专题 | 中国科学院新疆生态与地理研究所 |
| 作者单位 | 1.GIRADKAR S S (GIRA-Individual) 2.WANJARI S (WANJ-Individual) 3.RAUT P H (RAUT-Individual) 4.REDDY S C M (REDD-Individual) 5.NAYAK K (NAYA-Individual) 6.RAWAT S (RAWA-Individual) 7.DEEPA D (DEEP-Individual) 8.MARRI U (MARR-Individual) 9.RVS P (RVSP-Individual) 10.BATHALA S (BATH-Individual) 11.SIVANATHAN M (SIVA-Individual) 12.ALAUDDIN S (ALAU-Individual) |
| 推荐引用方式 GB/T 7714 | NAYAK K,GIRADKAR S S,WANJARI S,et al. Method for integration of deep learning and internet of things based plant disease detection for smart agriculture, involves empowering farmers to make informed decisions regarding crop management, for resulting in better productivity, and resilience against diseases. IN202511030630-A[P]. 2025. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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