Transforming Farming with CNNs: Accurate Crop and Weed Classification
Keywords:
CNN, Weed Crops, Decision Support, AgricultureAbstract
This research initiative proposes harnessing the power of Convolutional Neural Networks (CNNs) to advance accurate agriculture, a method driven by data to enhance farming efficiency and sustainability. The research aims to utilize CNNs to analyze images taken from agricultural fields, distinguishing between desired crops (such as brinjal, corn, onion, soybean, and sugarcane) and common weed species (like Amsinkia, Ambrosia, Cannabis, Trianthema portulacastrum, Otathus Maritimus, and erigeron). The main goal is to develop a decision support system that assists farmers in optimizing their resource management practices, particularly regarding the application of fertilizers and pesticides. By accurately identifying the composition of crops and weeds, the system can offer tailored recommendations for precisely allocating agricultural inputs, thus minimizing waste and environmental impact while maximizing yields. The research involves creating and validating the CNN-based classification model and integrating the decision support system into practical farming operations. The findings of this research could have significant implications for sustainable agriculture, presenting a technology-driven approach to improve productivity and soil health in contemporary farming methods.
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