A survey of high resolution image processing techniques for cereal crop growth monitoring

This paper presents a survey of image processing techniques proposed in the literature forextracting key cereal crop growth metrics from high spatial resolution, typically proximalimages. The descriptive crop growth metrics considered are: crop canopy cover, aboveground biomass, leaf area index (including green area index), chlorophyll content, andgrowth stage. The paper includes an overview of relevant fundamental image processingtechniques including camera types, colour spaces, colour indexes, and image segmentation. The descriptive crop growth metrics are defined. Reference methods for groundtruth measurement are described. Image processing methods for metric estimation aredescribed in detail. The performance of the methods is reviewed and compared. The surveyreveals limitations in image processing techniques for cereal crop monitoring such as lackof robustness to lighting conditions, camera position, and self-obstruction. Directions forfuture research to improve performance are identified.

Edge computing:A tractable model for smart agriculture?

Establishing food security remains a global challenge;it is thus a specific objective of the United Nations Sustainable Development Goals for 2030.Successfully delivering productive and sustainable agricultural systemsworldwide will form the foundations for overcoming this challenge.Smart agriculture is often perceived as one key enabler when considering the twin objectives of eliminating world hunger and undernourishment.The practical realization,deployment,and adoption of smart agricultural systems remain distant due to a confluence of technological,social,and economic factors.Edge computing offers a potentially tractable model for mainstreaming smart agriculture.A synergistic relationship exists,which,if harnessed productively,would increase the penetration of smart agricultural technologies across Majority-Minority world boundaries.The paper considers the prevailing context of global food security,smart agriculture and the pervasive issue of internet access.A survey of the state-of-the-art in research utilizing the Edgemodel of computing in agriculture is reported.Results of the survey confirm that the Edge model is actively explored in a number of agricultural domains.However,research is rooted in the prototype stage,and detailed studies are currently lacking.While potential is demonstrated,several systemic challenges must be addressed to manifest meaningful impact at the farm level.

Service design for climate-smart agriculture

Holistic information systems for climate-smart agriculture demands the seamless integration of various categories of climate,meteorological and weather data.Any actor in the agricultural value chain may harness weather forecasts at the short and medium-range,local weather history,and prevailing climatic conditions,to inform decision-making.Weather is fundamental to many day-to-day operations,especially at farm-level,influencing decision-making at various spatial and temporal scales.Many operational decisions ideally require hyper-localized service provision.In practice,integrating weather information into decision-support services demands a comprehensive understanding of various categories of weather-related data,their genesis,as well as the specific standards and data formats used by the meteorological community.This paper considers the weather as a crucial context for the delivery of farm-level operational services in smart agriculture,highlighting critical issues for reflection by system designers during the service design and implementation phases.