|
|
|
|
|
Zhiqing Guo, Xiaohui Chen, Ming Li, Yucheng Chi and Dongyuan Shi
Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction me...
ver más
|
|
|
|
|
|
|
|
|
Qianjing Li, Jia Tian and Qingjiu Tian
The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it remain...
ver más
|
|
|
|
|
|
|
|
|
Zhihong Zhang, Chaowei Huang, Xing Xu, Lizhe Ma, Zhou Yang and Jieli Duan
Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millime...
ver más
|
|
|
|
|
|
|
|
|
Teerath Rai, Nicole Lee, Martin Williams II, Adam Davis, María B. Villamil and Hamze Dokoohaki
Field research for exploring the impact of winter cover crops (WCCs) integration into cropping systems is resource intensive, time-consuming and offers limited application beyond the study area. To bridge this gap, we used the APSIM model, to simulate co...
ver más
|
|
|
|
|
|
|
|
|
Yuzhe Bai, Fengjun Hou, Xinyuan Fan, Weifan Lin, Jinghan Lu, Junyu Zhou, Dongchen Fan and Lin Li
With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on ...
ver más
|
|
|
|