Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System
Source abstract
Abstract
Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease datasets is used to detect weeds, pesticides, and diseases in the crop. The proposed model achieved an average accuracy of 95%, 97%, and 98% in plant seedlings, pests, and plant leaf disease detection, respectively. The system can help farmers pinpoint the precise measures required at the right time to increase yields.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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