Narrow Results

Wheat spike detection is a crucial component of wheat yield prediction. In this study, n lightweight and efficient wheat spike detection model is proposed. The model employs a novel Wheat Spike Net Block (WSNB) within a lightweight network architecture, integrating Depth-Wise Convolution (DW-Conv) and Efficient Window Multi-Head Self-Attention (EW-MHSA) to rapidly process images and accurately identify wheat spikes, even under compact small target conditions. The model is equipped with four detection heads to effectively handle targets of varying scales and incorporates the innovative EMF-IOU loss function for refined bounding box estimation. Tested on a self-constructed Shangluo winter wheat dataset, the model achieves a detection speed of 96.1 FPS on NVIDIA Tesla V100 and mAP@0.5 of 95.3%, surpassing YOLOv5, EfficientV2, YOlOX,transformer, and MobileVIt3 in terms of accuracy and efficiency. The model’s performance across diverse hardware platforms highlights its potential for practical implementation in real-time wheat yield estimation and precision agriculture.

The application of neural networks (NNs) to problems of prediction has become increasingly popular. This paper presents a modified hybrid adaptive neural network with revised adaptive smoothing errors, based on a genetic algorithm, and using modified adaptive relaxation to build a learning system for complex problem solving in yield prediction. This system predicts weekly yield values of a tomato crop using environmental variables measured inside the greenhouse as inputs. The proposed learning system is an intelligent computing technique and the numerical values of the neural network connection weights are modified through a training algorithm, using a modified optimization approach. The paper further presents an analysis of the convergence rate of the error in a neural network. The method is evaluated using datasets from a tomato producer, so as to test the predictive ability of the method and compare it with standard models. The results show a comparatively good level of accuracy.