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Punjab Startup's Device Can Tell How Safe Your Milk is in 40 Secs With 99% Accuracy


How safe is the milk you drink? In a 2020 report published by the Consumer Guidance Society of India (CGSI), 79% of branded or loosely packed milk in Maharashtra was adulterated. In other words, barely 21% of the samples tested by CGSI complied with the standards specified by the Food Safety and Standard Authority of India (FSSAI). The problem of milk adulteration is not confined to only one state. In states like Punjab, for example, there are regular reports of raids being conducted by the state against illegal factories making spurious milk and milk products.

Addressing the human cost in a changing climate


Climate change is leading to systemic and existential impacts, and evidence is mounting that these can result in the displacement of human populations. There is a rapidly growing demand for comprehensive risk assessments that include displacement and its associated costs to inform humanitarian response and national planning and coordination. However, owing to complex causation, missing and incomplete data, and the political nature of the issue, the longer-term economic impacts of disaster- and climate-related displacement remain largely hidden. Current approaches are rarely ex ante and prospective and do not consider systemic risk management. Not surprisingly, response-based approaches have shown mixed results, repeatedly demanding substantial resources while not addressing the root causes of displacement. Climate change is not only affecting the intensity, frequency, and duration of hazards that trigger displacement but is also eroding already fragile livelihoods and ecosystems, acting as an aggravator of existing vulnerability and contributing to chronic poverty and conflict in affected countries ([ 1 ][1]). Although disaster risk reduction as a cross-sectoral issue has gained considerable attention over the past two decades, disaster displacement risk is still not fully integrated in national policies and planning. Out of 46 countries included in the 2020 Internal Displacement Index, most acknowledge disaster displacement in principle and have climate policies or national adaptation plans in place. However, only 27 recognize the link between the gradual impacts of climate change and displacement ([ 2 ][2]). With an evidence-based, longer-term vision and investments, climate-related displacement— the forced movement of people in response to a hazard—can be averted and replaced by a range of measures such as planned relocation that is voluntary (at least to a large degree) and financially supported, or by building the resilience of at-risk populations, reducing vulnerability to such an extent that moving is not required. What is missing is a risk-informed framework for country-led, forward-looking approaches to make the case for substantial investment in effective risk reduction, durable solutions for those displaced, and the prevention of new displacement. Applied risk science, using probabilistic models and large empirical datasets compiled over the years, combined with insights from local empirical research and community assessments, now offers the opportunity for a step change in informed decision-making. For example, the shift from deterministic disaster risk assessments, based on historical data, to state-of-the-art probabilistic modeling used by the insurance industry, calibrated with historical data but including randomness to encompass all possible scenarios, presents a notable advance in risk science that is yet to be fully applied to displacement risk. New tools and risk modeling platforms, such as CLIMADA run by ETH Zürich or CAPRA of the World Bank, can now be adapted for displacement risk assessments. Further, assessing the social and economic cost of displacement can provide incentives for transformational action and change, from mere response to disaster displacement to proactively addressing vulnerability and exposure, thereby reducing displacement risk. Disaster displacement is a global reality and everyday occurrence. Millions of disaster displacements have been systematically recorded since 2008—on average, 24.5 million new movements every year ([ 3 ][3]). Weather-related hazards account for almost 90% of all these displacements ([ 2 ][2]), with climate change and the increasing concentration of populations in areas exposed to storms and floods, coupled with socioeconomic drivers of vulnerability, meaning that more people are at risk of being displaced. Demographic, historical, political, and socioeconomic factors determine whether people can withstand the impacts of a physical hazard or environmental stressor or have to leave their homes. Climate change interacts with all of these factors, particularly where resources and the capacities of humans and systems are already stretched ([ 4 ][4]). For example, sea level rise results in loss of land in coastal areas and low-lying atolls of island states, forcing communities to retreat or leave the land altogether. Salinization can reduce crop yields, undermine arable land and freshwater availability, and force people to move. Increasing temperatures affect soil moisture and degradation, which make the soil susceptible to nutrient loss and erosion, thereby destroying the livelihood basis for rural communities. Glacial retreat and melt, loss of biodiversity, and land and forest degradation mean decreased ecosystem services and provisioning services, pushing people to move. Because climate change can also alter the intensity, frequency, and duration of hazard events, climate anomalies and more devastating sudden-onset disasters may follow. Most of the impacts of climate change only result in displacement for those vulnerable to them. This essential point is repeatedly forgotten, with important policy implications ([ 5 ][5]). A prosperous farmer with access to drip irrigation and fertilizers, reliable buyers, loans, and insurance will not be as affected by changes in rainfall patterns as a smallholder subsistence farmer relying on the regularity of seasonal rains or a pastoralist in search of pasture for his herd. An urban dweller with an office job and regular income will not need to leave his home because of the loss of mangroves, which are providing sustenance to millions in coastal communities. Nonetheless, although individual vulnerability leads to a risk of adverse displacement outcomes, disaster and climate risks are increasingly becoming systemic because high-level and widespread impacts may ripple through social and economic networks, incurring further adverse micro and macro impacts and disruptions ([ 6 ][6]). Climate change is thus a displacement trigger in its own right (e.g., loss of coastlines through sea level rise and coastal erosion), a visible aggravator (e.g., when livelihoods are eroded because of soil degradation and loss of ecosystem services), and a hidden aggravator (e.g., increasing the intensity of cyclonic winds and shifting rainfall patterns that result in floods). But the impacts of climate change interact with broader changes in the physical and social environment, resulting in potentially rising costs associated with future displacement. ![Figure][7] Global disaster displacement risk relative to population size Average Annual Displacement (AAD) risk is a compact metric that represents the estimated effect, accumulated over a long time frame, of future small to medium and extreme events and estimates the likely displacement associated with them on a yearly basis for sudden-onset hazards such as tsunamis, cyclonic winds, storm surges, and riverine floods. See ([ 10 ][8]) for details. Each country's AAD risk relative to its population size is shown (expected annual displacements / 10,000 people). Country income group classification from the World Bank. GRAPHIC: N. DESAI/ SCIENCE BASED ON B. DESAI ET AL. Disaster displacement often undermines the welfare and well-being of affected individuals and communities and can also incur a substantial social and economic burden on countries. Although many countries have begun to plan for t he risk of extreme events in one way or another, governments typically do not formally account for displacement risk and their associated costs in national development plans and annual budgets of line ministries. Even without taking into account the aggravating forces of climate change, there is growing evidence that displacement not only severely disrupts the lives of those forced to flee their homes but also has an economic impact on local communities and national economies ([ 7 ][9]). The direct cost of providing every internally displaced person (totaling more than 55 million in 2020) with support for housing, education, health, and security has been estimated at US$370 per person per year, accumulating to more than US$20.5 billion for 2020 ([ 2 ][2]). These figures are mostly based on information available from protracted conflict-related displacement situations because the economic impacts of displacements linked with disasters and climate change usually go unrecorded. A key knowledge gap exists here because only limited event-based or nationally aggregated data is available on how long people remain displaced after a disaster, despite ample evidence that this type of displacement is often long-term and can become protracted ([ 2 ][2]). These impacts can add up to billions of dollars worldwide. Each time one person is displaced, even for a few days, costs arise for transportation, shelter, food and nonfood items, and the loss of income if the person cannot continue their usual work. Adding in long-term consequences, such as lack of schooling, training, and on-the-job experience, increases this economic impact. These costs should be on national balance sheets but are instead most often borne by communities themselves, by local governments that have to divert already limited development funds to response, and by humanitarian actors. In the face of increasingly severe disaster- and climate-related displacement, these costs are only set to rise. The highest economic impacts usually stem from the loss of income and the need to provide displaced people with shelter and health care. Disaster-resilient housing and livelihoods, as well as strong primary health care systems, are also where investments are needed most ahead of disaster events to reduce displacement and enable lasting solutions. By nature of its mandate, humanitarian response is not set up to invest in resilient livelihoods or infrastructure and service development. It is not only low-income nations that are at risk of economic impacts due to displacement. During the 2019–2020 bushfires in Australia, the loss of economic production as a result of people missing just one day of work during displacement was estimated to be about US$510 per person ([ 8 ][10]). These costs add up, particularly if a disaster causes considerable housing destruction, which may delay people from returning to their homes for months. The cost of covering housing needs resulting from Australia's Black Summer bushfires was estimated to be between US$44 million and US$52 million for a year, posing a substantial financial burden, given that previous recovery efforts indicate that it can take people between 1 and 4 years to rebuild their homes ([ 8 ][10]). These numbers and examples from across the globe highlight that we need to get better at understanding and assessing the nature and scale of disaster displacement risk. The coverage and detail of relevant datasets have improved, and various models and approaches exist at regional and global scales, although their time frames, methods, and resulting estimates vary enormously. For example, the World Bank, using a gravity model and new data on climate change, water availability, and crop production, has estimated that slow-onset climate hazards such as water scarcity and declining crop yields could lead to more than 100 million additional internal migrants in Latin America, South Asia, and sub-Saharan Africa by 2050 should neither accelerating climate impacts nor unequal development be adequately addressed ([ 9 ][11]). In many such assessments, there is a strong focus on environmental stressors and hazards, and on climate change's impacts on their intensity and frequency. This may have potentially resulted in inflated numbers and certainly in an inflated perception regarding the role of climate change in the dynamics of human mobility and forced movements today and in the coming decades. Estimates from a probabilistic model that takes housing rendered uninhabitable as a proxy for displacement in sudden-onset disasters, such as floods and cyclones, suggest that an average of around 14 million displacements can be expected each year (a conservative approach that is highly likely to be an underestimate) ([ 10 ][8]). This displacement risk is heavily concentrated in the Asia-Pacific region, where both exposure and vulnerability are high. Even in relative terms—that is, numbers of potential displacements in relation to population size—displacement risk is high not only for South and East Asia but also for Pacific and other small island states (see the first figure). Climate change as well as changes in population size and composition and of key social and economic indicators all affect how this displacement risk may change in the future. According to probabilistic, spatially explicit risk modeling that uses ensembles of climate models and hydrodynamic modeling to quantify flood hazard, is calibrated on past events, and incorporates commonly used climate change and development scenarios, rapidly increasing exposure due to population growth may be the largest driver of displacement risk in the future ([ 11 ][12]). Nevertheless, this strong role of population size should not overshadow the fact that the substantial increase related to climate change is not trivial (see the second figure). New assessments show that we can expect a 50% increase in displacement risk related to floods for each degree of temperature increase ([ 11 ][12]). Although, currently, various epistemic uncertainties need to be reckoned with, such projections serve to illustrate the future burden to consider in a rapidly warming and changing climate. Beyond probabilistic and deterministic disaster displacement risk models, there are other modeling approaches that can increasingly be put to the task. Agent-based network models can assess individual-level impacts and costs through a bottom-up methodology that can reflect how shocks to one part of a system (community, economy, country, or region) can cascade through the whole system and also spill over into other systems ([ 12 ][13]). Further, a system dynamics approach can describe in a relatively comprehensive manner the relationships between a wide range of dimensions and indicators, although it requires granular datasets that are often unavailable and is highly cost- and labor-intensive to develop. ![Figure][7] Change in flood displacement risk Shaded areas show different scenarios of flood displacement risk based on a range of climate and hydrological models, relative to historical baseline. The width of the shading represents an estimate of the uncertainty induced by natural climate variability and limitations in current understanding of the climate system and hydrological systems. Dashed lines show the average values across models. Historical baseline is defined by the average flood hazard frequency and intensity from 1976 to 2005, combined with population data for 2000. RCPs reflect different trajectories of variation in atmospheric GHG concentrations. SSPs reflect different scenarios of global socioeconomic development. Modified from ([ 11 ][12]). GRAPHIC: KELLIE HOLOSKI/ SCIENCE BASED ON KAM ET AL. ([ 11 ][12]) Finally, integrating risk estimates with analysis of public finance allows quantification of the relevance and “additionality” of internal displacement impacts on governments' (and often donors') budgets. First attempts at undertaking this analysis, adapting the International Institute for Applied Systems Analysis (IIASA) catastrophe simulation model (CatSim) in support of public financing strategies in pre- and postdisaster contexts, have shown that the cost of internal displacement can substantially increase national and global budget gaps (fiscal gaps) and the chance of budget crises ([ 13 ][14]). F or example, in Bangladesh, a disaster with a return period of 50 years can be expected to incur costs related to internal displacement of nearly US$4.1 billion per year of subsequent displacement; a smaller magnitude but more frequent disaster with a return period of 10 years would incur more than US$1 billion. The estimated possible amount of funding that the country may be able to divert from existing development budgets and credit buffers adds up to just over US$1 billion of fiscal resilience, which means that Bangladesh is likely to be unable to cover the costs associated with internal displacement for events that occur every 10 years on average. Further estimates of such costs can provide the basis for making the case for preventive action and for developing appropriate financial instruments such as national reserve funds, enhanced social protection schemes, and catastrophe bonds, as well as regional or global sovereign insurance pools ([ 14 ][15]). Beyond these first steps in developing basic estimates of the costs, further work is required to better understand who bears these and how benefits from improved policies would be distributed across different segments of society. Comprehensive risk assessments that account for displacement risk and estimate its economic costs signal a need to improve coordination on budget allocations and cooperation in program execution across ministries and public and private sectors. This would enable the explicit inclusion of these contingent risks into budget stress-testing procedures and other risk-management planning processes. It would also provide incentives for managing risk with an ex ante approach, because it anticipates the ex post consequences and trade-offs involved in responding to shocks ([ 13 ][14]). Risk assessments should help communities and local and national governments grappling with immediate displacement risk or the prospect of intensifying natural hazards or loss of territory or habitats. More financing must be made available for localized, granular displacement risk assessments, which municipalities can use to inform urban development plans, zoning regulations, and local building codes and for forward-looking, long-term planning for relocation where necessary. Recent attempts at providing a measure for displacement risk and its impacts are only the first step. In the coming years, further investment should build on the promises of longer-term risk modeling and couple its results with impact assessments so that countries can build displacement estimates into their multiyear development plans ([ 15 ][16]). Understanding needs and priorities in the decision-making processes of affected populations, institutional capacities, and socioeconomic dynamics, even if less systematically assessed, will be at least as important at indicating what the future holds. Given the scope and complexity of the problem, a pluralistic methodological setup is required to contribute to a better understanding of displacement risk and to inform effective policy and response under a broad range of circumstances. 1. [↵][17]Intergovernmental Panel on Climate Change (IPCC), Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, Eds. (IPCC, 2014). 2. [↵][18]Internal Displacement Monitoring Centre (IDMC), Global Report on Internal Displacement 2021 (2021); [][19]. 3. [↵][20]IDMC, Global Internal Displacement Database; [][21]. 4. [↵][22]1. M. Brzoska, 2. C. Fröhlich , Migr. Dev. 5, 190 (2016). [OpenUrl][23][CrossRef][24] 5. [↵][25]Economics of Climate Adaptation (ECA), “Shaping climate-resilient development: A framework for decision-making. A report of the Economics of Climate Adaptation Working Group” (ECA, 2009); [\_of\_Climate\_Adaptation\_ECA.pdf][26]. 6. [↵][27]1. C. Raymond et al ., Nat. Clim. Chang. 10, 611 (2020). [OpenUrl][28] 7. [↵][29]1. S. Ambrus 1. C. Cazabat, 2. L. Yasukawa , “Unveiling the cost of internal displacement. 2020 report,” S. Ambrus, Ed. (IDMC, 2020); [\_CostEstimate\_final.pdf][30]. 8. [↵][31]1. J. Lennard 1. E. du Parc, 2. L. Yasukawa , “The 2019–2020 Australian bushfires: From temporary evacuation to longer-term displacement,” J. Lennard, Ed. (IDMC, 2020); [][32]. 9. [↵][33]1. K. K. Rigaud et al ., “Groundswell: Preparing for internal climate migration” (World Bank, 2018); . 10. [↵][34]IDMC, “Global disaster displacement risk – A baseline for future work” (2017); [][35]. 11. [↵][36]1. P. M. Kam et al ., Environ. Res. Lett. 16, 044026 (2020). [OpenUrl][37] 12. [↵][38]1. A. Naqvi, 2. F. Gaupp, 3. S. Hochrainer-Stigler , OR Spectrum 42, 727 (2020). [OpenUrl][39] 13. [↵][40]IDMC, IIASA, “Points of no return: Estimating governments' fiscal resilience to internal displacement” (IDMC, 2020); [][41]. 14. [↵][42]1. J. Linnerooth-Bayer, 2. S. Hochrainer-Stigler , Clim. Change 133, 85 (2015). [OpenUrl][43] 15. [↵][44]1. S. Hochrainer-Stigler et al ., Int. J. Disaster Risk Reduct. 24, 482 (2017). [OpenUrl][45] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: pending:yes [8]: #ref-10 [9]: #ref-7 [10]: #ref-8 [11]: #ref-9 [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: #xref-ref-2-1 "View reference 2 in text" [19]: [20]: #xref-ref-3-1 "View reference 3 in text" [21]: [22]: #xref-ref-4-1 "View reference 4 in text" [23]: {openurl}?query=rft.jtitle%253DMigr.%2BDev.%26rft.volume%253D5%26rft.spage%253D190%26rft_id%253Dinfo%253Adoi%252F10.1080%252F21632324.2015.1022973%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: /lookup/external-ref?access_num=10.1080/21632324.2015.1022973&link_type=DOI [25]: #xref-ref-5-1 "View reference 5 in text" [26]: [27]: #xref-ref-6-1 "View reference 6 in text" [28]: {openurl}?query=rft.jtitle%253DNat.%2BClim.%2BChang.%26rft.volume%253D10%26rft.spage%253D611%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [29]: #xref-ref-7-1 "View reference 7 in text" [30]: [31]: #xref-ref-8-1 "View reference 8 in text" [32]: [33]: #xref-ref-9-1 "View reference 9 in text" [34]: #xref-ref-10-1 "View reference 10 in text" [35]: [36]: #xref-ref-11-1 "View reference 11 in text" [37]: {openurl}?query=rft.jtitle%253DEnviron.%2BRes.%2BLett.%26rft.volume%253D16%26rft.spage%253D044026%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: #xref-ref-12-1 "View reference 12 in text" [39]: {openurl}?query=rft.jtitle%253DOR%2BSpectrum%26rft.volume%253D42%26rft.spage%253D727%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: #xref-ref-13-1 "View reference 13 in text" [41]: [42]: #xref-ref-14-1 "View reference 14 in text" [43]: {openurl}?query=rft.jtitle%253DClim.%2BChange%26rft.volume%253D133%26rft.spage%253D85%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: #xref-ref-15-1 "View reference 15 in text" [45]: {openurl}?query=rft.jtitle%253DInt.%2BJ.%2BDisaster%2BRisk%2BReduct.%26rft.volume%253D24%26rft.spage%253D482%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx

Artificial Intelligence Gets Real for Big Firms


As artificial intelligence continues to move into the mainstream, companies are combining AI and big data to build and design better products, react faster to changing market conditions, and protect consumers from fraud. According to experts at EmTech Digital, MIT Technology Review's annual event on artificial intelligence, big data plus AI creates a foundation for more intelligent products and services -- ones that initiate maintenance procedures before something breaks, perform more precise operations, or automatically recalibrate resources to meet changing demand and usage patterns. While AI and big data pave the way for such evolutionary use cases, the pair do not constitute a business strategy on their own accord. "The question is how do you use AI right or use it wisely," said panelist Ed McLaughlin, president of operations and technology for Mastercard. "The biggest lesson learned is how to take these powerful tools and start backward from the problem," McLaughlin said.

Welcome! You are invited to join a webinar: How to employ Automated Machine Learning to Predict the Best Quality Potato Chip/Crisp. After registering, you will receive a confirmation email about joining the webinar.


We will show how a team of researchers applied JADBio’s Automated Machine Learning (AutoML) platform to predict potatoes' susceptibility to bruising and also its potential for coloration during chip/crisp processing. The aim was to differentiate between potatoes that would be less prone to bruising from those that would more easily bruise during mechanical handling. Another goal was to successfully predict the potatoes’ potential susceptibility to acrylamide formation during chip/crisp processing due to the Maillard reaction. In this webinar series, Aris Karanikas (Business Development Officer) and Vincenzo Lagani (VP of Bioinformatics) at JADBio will demonstrate the advanced capabilities of AutoML to assist researchers and agronomists in data analysis. They will explain how to apply the JADBio platform based on real-life agricultural case-studies. Artificial intelligence (AI) and application of machine learning models are currently trending in the agriculture industry, and you will learn how it can help you to make better analytic decisions and improve your data interpretation efficiency. By attending this webinar, you will discover: - How you can analyze and classify your potato samples, without extensive data science knowledge - Discover which specific features play a role in high quality potatoes, along with their relative strength as predictors - Understand how relevant sets of equivalent predictors can also affect the desired result - How to apply your model on all future potato samples - How AutoML can help the agriculture industry in more efficient seed production, breeding, and many other sectors of the industry Who is this Webinar for: - Researchers - Agronomists - Farmers and anyone who needs to discover how they can utilize machine learning to predict crop performance, without the need to learn data science or acquire programming skills. Take-away: All attendees will receive a fully functional monthly licence (free of charge) for JADBio AutoML

New agricultural robots kill individual weeds with electricity


Small Robot Company (SRC), a British agritech startup for sustainable farming, has developed AI-enabled robots – named Tom, Dick and Harry – that identify and kill individual weeds with electricity. These agricultural robots could reduce the use of harmful chemicals and heavy machinery, paving the way for a new approach to sustainable crop farming. The startup has been working on automated weed killers since 2017, and this April officially launched Tom, the first commercial robot currently operating on three UK farms. Dick is still in the prototype phase, and Harry is still in development. Small Robot company says the robot Tom is capable of scanning around 20 Hectares per day, collecting about six terabytes of data in an 8-hour shift to identify the crops, spots undesirable weeds – using "Wilma," an artificial intelligence operating system.

New Weeding Robot Could Boost Sustainable Agriculture and Increase Food Production


Humanity needs food for survival, and maintaining consistent crop yields is essential. Recently, the world of artificial intelligence (AI) entered the agricultural industry, providing farmers with sustainable solutions. Engineers developed an autonomous robot that eliminates weeds and increases food production. Weeds are difficult to manage because of their unpredictable growing patterns. Insect and disease management have designated tools and tactics where weed control differs.

Watch Drones Fly Through a Fake Forest Without Crashing


The mathematical engineer and robotics PhD student from the Swiss Federal Institute of Technology Lausanne, or EPFL, had already built a computer model to simulate the trajectories of five autonomous quadcopters flying through a dense forest without hitting anything. But an errant copter wouldn't survive a tête-à-tête with a physical tree. So Soria built a fake forest the size of a bedroom. Motion-capture cameras lined a rail hanging above the space to track the movement of the quadcopters. And for "trees," Soria settled on a grid of eight green collapsible kids' play tunnels from Ikea, made of a soft fabric.

How AI Can Be Used in Agriculture Sector for Higher Productivity?


Artificial Intelligence (AI) with help of Machine Learning (ML) can create an automated model for different fields. Agriculture and farming are one of the them, provides the food to the majority of populace on this earth that also need such technology to boost its productivity and efficiency. Machine learning is the branch of AI, and such AI models cannot be developed without using the machine learning process. The ML process involves using the training datasets into an algorithms to learn the certain patterns and predict the results learnt from such data sets. And when such models are trained enough to work automatically when exposed to new data and take actions without help of humans.

AI is having a big impact, but not how you think


Artificial intelligence has grabbed headlines for the past few years, but too often the press oversells the risks and rewards of AI. We read about AI's inevitable bias, and its deadly use in war. Of course, we also read the positive, like a Google computer beating the world's best Go players. But these stories fail to accurately reflect the best uses of AI today. I wrote years ago that IBM needed to stop pitching its Watson as a miracle cure for most everything, and instead position it for more pedestrian tasks. In like manner, we'd do better to celebrate AI adoption in small steps that add up to major savings--like food and waste and other sectors.

How AI innovation is improving agricultural efficiency


As I noted recently, organizations often find the biggest success through small steps with artificial intelligence. There are many examples of this at work, but Linux offers a great one. Linux started out as a student desktop experiment before it creeped slowly into companies as a reliable print server before eventually taking over the data center and the cloud (and Mars--it's on both the Chinese and U.S. rovers there). Incremental steps can add up to big things. In the area of food production, it needs to.