MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions.

Identification and diagnosis of tobacco diseases are prerequisites for the scientific prevention and control of these ailments. To address the limitations of traditional methods, such as weak generalization and sensitivity to noise in segmenting tobacco leaf lesions, this study focused on four tobacco diseases: angular leaf spot, brown spot, wildfire disease, and frog eye disease. Building upon the Unet architecture, we developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet) by enhancing the feature extraction module and integrating attention mechanisms.

Automatic detection and counting of wheat spike based on DMseg-Count.

The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike.

Coupled one-off alternate furrow irrigation with nitrogen topdressing at jointing optimizes soil nitrate-N distribution and wheat nitrogen productivity in dryland.

The judicious management of water and nitrogen (N) is pivotal for augmenting crop productivity and N use efficiency, while also mitigating environmental concerns. With the advent of the High-Farmland Construction Program in China, one-off irrigation has become feasible for most dryland fields, presenting a novel opportunity to explore the synergistic strategies of water and N management. This study delves into the impact of one-off alternate furrow irrigation (AFI) and topdressing N fertilizer (TN) on soil nitrate-N distribution, and N productivity-including plant N accumulation, translocation, and allocation, and grain yield, protein content, N use efficiency of winter wheat (Triticum aestivum L.

Automatic grading evaluation of winter wheat lodging based on deep learning.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results.