Zhengyang Wu , Hongwen Li , Jin He , Xu Zhang , Caiyun Lu , Chao Wang , Dejian Zhang , Shan Jiang , Hongdao Shan , Rongrong Li , Zongfu Yang , Sitong Duan
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引用次数: 0
Abstract
The application of organic amendments is considered a green and sustainable method to make up for the loss of organic matter in cultivated soils, where the soil heterogeneity due to the uneven spatial distribution of organic amendments in the soil should not be ignored. This study used brilliant blue stains to trace the liquid amendments in the application of liquid amendments (ALA). A total of 22 digital images of vertical profiles were used to quantify the distribution of amendments for the experiment. Simulations replicated the ALA by the discrete element method (DEM), where 22 sliced subspaces of the same size as the experimental condition were used to quantify the distribution of the simulated amendments. Each sliced subspace was further divided into 100 minimal subspaces, and it was calculated by an amendment mixing index (AMI) which was a normalized index given a sign. The results showed that the simulation accurately reproduces the AMIs in the sliced subspaces with an average relative error of 1.25 % to 23.79 %. The AMI assigned to the symbol reflects the mixing of liquid amendments in the minimal subspace. An approach based on digital image processing and an approach based on DEM simulation that could be used to analyze the mixing of liquid amendments quantitatively has been developed. They can visually characterize the mixing of liquid amendments.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.