Deep Learning Approach: Precision Agriculture Advancements Through Accurate Segmentation of Crop and Weed Density

Utilizing deep learning in the segmentation of crop and weed density is a huge leap in the sector of precision agriculture. The proposed model consists of a convolutional neural network and the EfficientNet architecture to come up with a model that performs excellent on the segmentation of identifying and measuring the density of crop and weed. This research was driven towards addressing the complexities and variations in agricultural images and created the model that can outperform YOLO v5s, AlexNet, GBMs, CNNs, U-Net++, and DNNs. The study is guided by success consideration of the model performance that will imply the strength and power of the proposed model to be used in precision agriculture. The presented model has shown excellent performance including accuracy equal to 98.54%, precision equal to 97.87%, recall 96.45%, and F1 equal to 97.99%. It is possible due to the use of an 80:20 data split for training and validation. By using the presented method, the ability to generalize the model and to ensure the risk of overfitting are greatly reduced. Moreover, the overall accuracy is high, whereas there is a proper precision-recall balance. This feature is critical for the practical application of agricultural models. Additionally, following the presented approach is important for preserving computational efficiency through the use of the EfficientNet architecture. It means that the developed model can be used in a broad array of real-life applications that are characterized by disparate processing requirements. This novel deep learning approach offers opportunities for better crop management, allocation of resources, and weed control, which, in turn, will result in remarkable improvements in agricultural productivity and sustainability. The efficiency of this model clearly proves the great effect of deep learning on the resolving of the most intricate issues in the art of precision agriculture.