This study develops an automated crop growth detection APP, with the functionality to access cadastral data for target field, that was be used a satellite-imagery-based field survey. A total of 735 ground-truth records cabbage cultivation areas in Yunlin were collected via implemented APP order train deep learning model make accurate predictions stages from 0 70 days. regression analysis perfor...

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize features. In this study, a hybrid framework is designed for crop using feature fusion. Convolutional Neural Network (CNN) used high level features are fused with HCF. Cepstral coefficients of RGB images presented as one along other po...

The real-time target detection of crop pests can help detect and control in time. In this study, we built a lightweight agricultural pest identification method based on modified Yolov5s reconstructed the original backbone network tandem with MobileNetV3 to considerably reduce number parameters model. At same time, ECA attention mechanism was introduced into shallow meet aim effectively enhancin...

An off-the-shelf low cost laser sensor was tested and evaluated both in laboratory and field conditions. The sensor identified the angular and straight edges of the laboratory test surface and replicated the straight edge profile with an error of 4%. In field conditions, the sensor identified three types of cut crop edges (wheat, alfalfa and corn) and replicated distinct shapes (triangle, curve...

Around the world due to pests and pathogens almost 50% of agricultural produce is lost which so alarming given fact that many people die everyday starvation in poor nations. Crop diseases disturb normal growth physiological processes. It estimated every year 20-40% crop loss reported and, some cases, whole production gets destroyed. So, higher yield for sustainable agriculture it important iden...