AI-Driven Weed Classification for Improved Cotton Farming in Sindh, Pakistan
Keywords:
Precision agriculture, Weed detection, Machine learning, IoT, CottonWeedsAbstract
This research study proclaims the combination of artificial intelligence and also IoT in precision agriculture, highlighting weed discovery plus cotton plant monitoring in Sindh, Pakistan. The uniqueness lies in creating a deep learning-based computer system vision application to develop a durable real-time weed category system, dealing with a problem not formerly solved. The study entailed gathering datasets utilizing mobile cams under varied ecological problems. A CNN version was educated utilizing the open-source Cotton Weeds dataset, annotated with clinical problems such as Broadleaf and Horse Purslane. Examinations used a Wireless Visual Sensor Network (WVSN) with Raspberry Pi for real-time photo catching as well as category. The CNN version, readjusted to identify in between cotton along with Horse Purslane weed accomplished a precision of 86% and also an ROC AUC rating of 0.93. Efficiency metrics consisting of precision-recall, as well as F1 rating, suggest the model's viability for various other weed category jobs. Nonetheless, obstacles such as photo top-quality variants and also equipment constraints were kept in mind. The research ends that using artificial intelligence as well as IoT in farming can dramatically improve plant return plus assist lasting methods for future generations.
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