Cotton Plant Disease Prediction and Remedy Recommendation System

Vaidehi Manurkar, Sumedh Kulkarni, Suyash Rokade, Riddhi R. Mirajkar
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Abstract

In the dynamic context of India's pivotal cotton industry, we embark on a pioneering research endeavor that harnesses the formidable synergy of agriculture, state-of-the-art artificial intelligence, and cutting-edge computer vision technologies. Our work attempts to accomplish two goals: first, we will build a flexible and intelligent AI model that has been fine-tuned to quickly and correctly detect common cotton plant diseases from a collection of images; second, we will build an approachable and user-friendly platform that enables farmers to upload images of their sick cotton crops for quick analysis. Our research aspires to endow the agricultural community with timely, data-driven insights and customized recommendations, thereby elevating disease management and fostering sustainable practices that augment the resilience and prosperity of India's cherished cotton industry.
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棉花植物病害预测和补救建议系统
在印度举足轻重的棉花产业的动态背景下,我们开始了一项开创性的研究工作,利用农业、最先进的人工智能和尖端计算机视觉技术的强大协同作用。我们的工作试图实现两个目标:首先,我们将建立一个灵活、智能的人工智能模型,该模型经过微调,能够从图像集合中快速、正确地检测常见的棉花植物病害;其次,我们将建立一个平易近人、用户友好的平台,使农民能够上传患病棉花作物的图像,以便进行快速分析。我们的研究旨在为农业界提供及时、数据驱动的见解和定制化建议,从而提升病害管理水平,促进可持续发展实践,增强印度宝贵的棉花产业的复原力和繁荣。
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