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[会议论文 ] Multi-variant Analysis of Climatic Conditions and Soil Rainfall with Best Crop Prediction 进入全文
IEEE
Agriculture is an important job in the development of the nation’s economy. The climate along with other natural variations has to turn out to be a significant risk in the agricultural field. Machine Learning technology can be used for determining the best crop suitable for the climatic condition, soil condition from the data set for addressing this issue. Crop Yield Prediction includes anticipating the yield of the crop from accessible verifiable information like climate parameter, soil parameter, and notable yield. Such huge numbers of individuals are doing fertile agriculture by developing the yield on ill-advised soil. To actualize the application to recognize the sorts of oil, water wellspring of that land whether that land depends on downpour or bore water. Furthermore, recommend what of the crop is reasonable for that dirt. So, through this application to the individual to think about agriculture. It tends to be improved by the utilization of many mechanical assets, device, and methods. Find the kind of crop that is appropriate for that specific soil.
[会议论文 ] A Bayesian Deep Image Prior Downscaling Approach for High-resolution Soil Moisture Estimation 进入全文
IEEE
Soil moisture (SM) estimation is a critical part of environmental and agricultural monitoring, with satellite-based microwave remote sensing being the main SM source. However, the limited spatial resolution of most current remote sensing SM products reduces their utility for many applications such as evapotranspiration modeling and agriculture management. To address this issue, we propose a Bayesian deep image prior (BDIP) downscaling approach to estimate the high-resolution SM from satellite products. More specifically, the high-resolution soil moisture estimation problem is formulated as a maximum a posteriori (MAP) problem, and solved via a neural network comprising of a deep fully convolutional neural network (FCNN) for modeling the prior spatial correlation distribution of the underlying high-resolution SM variables, and a forward model characterizing the SM map degeneration process for modeling the data likelihood. As such, the proposed BDIP approach provides a statistical framework that integrates deep learning with forward modelling in a coherent manner for combining different sources of information, i.e., the knowledge in the forward model, the spatial correlation prior in FCNN architecture, and the remote sensing data and products. Experiments on the downscaling of Soil Moisture Active Passive SM products using the Moderate Resolution Imaging Spectroradiometer products show that SM maps estimated using the proposed method provide greater spatial detail information than other downscaling methods, with the SM estimates very close to in-situ measurements.
[学术文献 ] 植物无土栽培技术研究进展 进入全文
中国农业大学学报
为了解植物无土栽培技术的发展，以“无土栽培”、“基质栽培”、“雾培”和“水培”为关键词，依据Web of Science、Pub-Med和知网等数据库，检索了1989—2021年发表的相关文献，对无土栽培发展历程、主要技术以及未来趋势进行了总结和分析。结果表明：1)无土栽培包括水培、雾培、基质栽培等，其中成本低、操作简单的基质栽培是主要方式，而操作、成本均更高的雾培和水培在高效植物栽培工厂建设上潜力巨大；2)探索高效、节水、可持续有机种植技术，融合人工智能和物联网技术，发展适配常规环境和恶劣环境的智慧农业是发展趋势之一；3)针对室内及楼宇空间等个体化种植需求，发展小型化、家庭化、精致化、智能化无土栽培技术是发展趋势之二；4)密闭空间种植和太空种植技术的研究也将受到更多关注。
[前沿资讯 ] 内蒙古超额完成黑土地保护性耕作国家任务 进入全文
农民日报
记者日前从内蒙古农牧厅获悉，截至5月24日，全区黑土地保护性耕作实施面积达到1440.4万亩，已完成国家下达的1350万亩全年任务，超额完成90.4万亩。全区投入免耕播种机8469台，共有5511个实施主体参与保护性耕作作业。今年，内蒙古按照“稳步扩面、质量为先”原则，主推秸秆全量覆盖免少耕播种、秸秆部分覆盖免少耕播种和秸秆少量覆盖免少耕播种3种技术模式，实行差异化补助，做到高质多补。为推动黑土地保护性耕作规范实施，内蒙古注重全过程监管，紧扣春播关键农时节点，强化机具有效供给，充分运用智能监测终端，实现保护性耕作地块监测全覆盖，有力保证了实施质量。据了解，黑土地保护性耕作行动自2020年起在内蒙古启动实施，围绕落实保护性耕作“多覆盖、少动土”的核心技术要求，在东部四盟市34个旗县推广应用保护性耕作技术，实施面积持续扩大。2020年和2021年分别完成黑土地保护性耕作面积758.9万亩和1116.2万亩，为粮食稳产丰产和黑土地保护利用奠定坚实基础。
[会议论文 ] An Optimized Gaussian Extreme Learning Machine (GELM) for Predicting the Crop Yield using Soil Factors 进入全文
IEEE
Indian agriculture is extremely important and plays a predominant role in economy and employment. The agriculture has seen a significant technological transition because of data collection, environmental factors, crop selection, soil nutrients, pesticides and plant disease for making better farming decisions. This revolution in agriculture is addressed by using emerging technologies. Early detection and management of crop yield indicator problems can help to increase the yield and subsequent profit. Machine learning is an emerging technology used in agricultural research for yield prediction. To produce accurate results, a simplest and very fast optimized learning algorithm called GELM (Gaussian Extreme Learning Machine) classifier with different kinds of activation functions are used. For the soil dataset, the classifier is trained using 50 hidden neurons with different activation functions. The performance analysis of the system shows that gaussian extreme learning achieves an accuracy of 97% compared to other algorithms. This analysis helps in interpretation of results in efficient manner for any regional soil data.
[前沿资讯 ] 内蒙古五原县：加强耕地健康监测 守住农民“田饭碗” 进入全文
中国农网
初夏，河套小麦正值生长期，内蒙古自治区巴彦淖尔市五原县新公中镇永联村七组的小麦种植基地远远望去成片青绿，无限生机。全顺农民专业合作社的理事长郝存林一边清除着麦田中的杂草，一边查看麦苗长势。他向记者介绍说，几年前这块儿地还是村里撂荒的盐碱地，400多亩土地荒废无人耕种，一直是周边农户的心病。2020年，五原县自然资源局积极争取上级资金进行土地综合整治，将农村散落、闲置、低效的用地经过科学规划整治后建成连片的高标准农田，这让新公中镇永联村七组的闲散土地被重新修复“唤醒”，并引入合作社进行连片种植，昔日撂荒的土地变为致富田，种植小麦每亩可增收250元。
