Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield
- سال انتشار: 1402
- محل انتشار: فصلنامه مدیریت فناوری اطلاعات، دوره: 15، شماره: 5
- کد COI اختصاصی: JR_JITM-15-5_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 247
نویسندگان
Associate Professor, School of Information Technology & Engineering [SITE], VIT, Vellore, Tamil Nadu, India.
Research Scholar, School of Information Technology & Engineering [SITE], VIT, Vellore, Tamil Nadu, India.
چکیده
Agriculture is one of the essential sources of occupation and revenue in India. Conferring to existing statistics, most agriculturalists are facing severe losses due to poor farming yield. Farming activities are challenged by various environmental factors that affect agricultural productivity to a greater extent. The present farming situation is above the average of the process involves more biochemical bases for managing the diseases and other destructing facts. The foremost problems they are facing in day-to-day farming tasks are crop or plant diseases affecting productivity. Also, the growth of weeds along with field crops has been another challenge. The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. The result has been evaluated and observed through the performance evaluation metrics using confusion matrix, accuracy, precision, Sensitivity, specificity with the observations, research, and studies. The statistics have expressed the overall accuracy of ۹۸% by achieving the detection of diseases in plants and by removing the weeds that ruin the growth of plants.کلیدواژه ها
Machine learning, Statistical Techniques Hyperspectral Data, Image classification and accuracyاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.