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[前沿资讯] COVID-19 to increase adoption of digital agriculture 进入全文

Future Farming

Labour shortages and supply chain disruptions due to the COVID-19 pandemic increase adoption of digital agriculture, according to a report by MarketsandMarkets. In the latest report, called ”COVID-19 Impact on Digital Agriculture Market by Smart Farming Systems”, MarketsandMarkets concludes that the digital agriculture market will grow from USD 5.6 billion in 2020 to USD 6.2 billion by 2021.

[前沿资讯] World’s first autonomous farm robot fleet ready for 2022 进入全文

Farmers Weekly

The world’s first fleet of autonomous robots set to scan plants, kill weeds and drill crops is set to become commercially available for UK farmers in just two years. The Small Robot Company, based in Salisbury, is developing three autonomous farmbots (named Tom, Dick and Harry), which will map weeds, enabling targeted follow-up treatments in crops. Sam Watson Jones, co-founder of the group, said this is a major technical milestone for British agriculture, offering an end-to-end farm service operation for arable farmers.

[专业会议] 2020年(南京)中国人工智能+农业高峰论坛 进入全文

农业农村部农业物联网重点实验室

2020年中央一号文件指出,加快物联网、大数据、区块链、人工智能、第五代移动通信网络、智慧气象等现代信息技术在农业领域的应用。为了推动人工智能技术与农业现代化深度融合和发展,在中国农技推广中心的指导下,中国人工智能+农业组委会决定在“第五届中国(南京)国际智慧农业博览会”期间,于2020年8月13日举办“2020中国人工智能+农业高峰论坛”。论坛将邀请相关部委领导和智慧农业领域权威院士、专家、学者和企业家针对人工智能+农业政策支持、技术内容进行交流,共同探讨人工智能+农业发展趋势和前景

[学术文献] 基于无线传感器网络的环境噪声感知研究进展 进入全文

南京农业大学学报

环境噪声已经成为仅次于空气污染的第二大生态环境污染源,其持续危害人类健康、畜禽发育和农作物生长,严重阻碍生态环境的可持续发展。为获得详细噪声分布信息,世界各国陆续启动噪声地图战略计划。本文首先对我国环境噪声感知技术研究发展历程进行总结,并对研究现状进行分析。然后,从系统架构、低成本噪声传感器节点设计、噪声分类学与声源识别、能量捕获和移动噪声感知5个方面综述基于无线传感器网络的环境噪声感知国际研究发展动态。在此基础上,分析能量高效感知、轻量级多元噪声识别、无线多媒体可靠传输和噪声感知节点优化部署4个尚未解决的核心科学问题。最后,对未来研究方向进行展望。 

[学术文献] A knowledge-based approach to designing control strategies for agricultural pests 进入全文

Agricultural Systems

Chemical control of insect pests remains vital to agricultural productivity, but limited mechanistic understanding of the interactions between crop, pest and chemical control agent have restricted our capacity to respond to challenges such as the emergence of resistance and demands for tighter environmental regulation. Formulating effective control strategies that integrate chemical and non-chemical management for soil-dwelling pests is particularly problematic owing to the complexity of the soil-root-pest system and the variability that occurs between sites and between seasons. Here, we present a new concept, termed COMPASS, that integrates ecological knowledge on pest development and behaviour together with crop physiology and mechanistic understanding of chemical distribution and toxic action within the rhizosphere. The concept is tested using a two-dimensional systems model (COMPASS-Rootworm) that simulates root damage in maize from the corn rootworm Diabrotica spp. We evaluate COMPASS-Rootworm using 119 field trials that investigated the efficacy of insecticidal products and placement strategies at four sites in the USA over a period of ten years. Simulated root damage is consistent with measurements for 109 field trials. Moreover, we disentangle factors influencing root damage and pest control, including pest pressure, weather, insecticide distribution, and temporality between the emergence of crop roots and pests. The model can inform integrated pest management, optimize pest control strategies to reduce environmental burdens from pesticides, and improve the efficiency of insecticide development.

[学术文献] Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data 进入全文

Biosystems Engineering

Non-destructive estimates information on the desired properties of fruit without damaging them. The objective of this work is to present an algorithm for the automatic and non-destructive estimation of four maturity stages (unripe, half-ripe, ripe, or overripe) of Fuji apples (Malus Malus pumila) using both colour and spectral data from fruit. In order to extract spectral and colour data to train a proposed system, 170 samples of Fuji apples were collected. Colour and spectral features were extracted using a CR-400 Chroma Meter colorimeter and a custom set up. The second component a∗ of La∗b∗ colour space and near infrared (NIR) spectrum data in wavelength ranges of 535–560 nm, 835–855 nm, and 950–975 nm, were used to train the proposed algorithm. A hybrid artificial neural network-simulated annealing algorithm (ANN-SA) was used for classification purposes. A total of 1000 iterations were conducted to evaluate the reliability of the classification process. Results demonstrated that after training the correction classification rate (CCR, accuracy) was, at the best state, 100% (test set) using both colour and spectral data. The CCR of the four different classifiers were 93.27%, 99.62%, 98.55%, and 99.59%, for colour features, spectral data wavelength ranges of 535–560 nm, 835–855 nm, and 950–975 nm, respectively, over the test set. These results suggest that the proposed method is capable of the non-destructive estimation of different maturity stages of Fuji apple with a remarkable accuracy, in particular within the 535–560 nm wavelength range.

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