SoilMapr – Unleashing smallholder farmer potential

April 21, 2017vsaigal

Problem

With population growing exponentially around the world, the FAO estimates that world food production would need to rise by 70% by 2050. In particular, developing countries would have to increase output by 2X. In contrast, agricultural production in developing countries is lagging far behind the rest of the world. An estimate of corn production shows that yield in the United States are 5X of those in Africa.

A key factor driving this disparity is the low levels of mechanization in emerging economies across the agricultural value chain. In particular, farming practices tend to be reliant on unpredictable traditions (what does this mean? if it is what I think it is, maybe unreliable is a better word?). While high tech and precision farming are growing in the United States, where effective soil maps and understanding of environmental factors is enabling higher yield through lower resources, many resource-poor countries are limited by inconsistent rainfall, poor input quality, and historically ineffective farming practices.

A lack of basic understanding of the soil being cultivated on is a key deterrent. With most agricultural work in developing economies being done by smallholder farmers with less than 1-2 hectares, soil quality can vary significantly from one farm to another. However, practices being adopted tend to be standard across communities.

To be able to ensure that food production rises to meet the increasing demand from growing population, as well as ensure yield parity across the globe – technology must be harnessed.

Solution

SoilMapr will collect multidimensional data about soil through a low-cost device with sensors that can measure soil quality and nutrient mix. Small sensor devices will be placed in farmland, and through a combination of optical, electrochemical, and mechanical sensors will be able to create a soil profile.

The use of SoilMapr would include the following steps:

Before cultivation:

o Place the SoilMapr sensor in the ground for 30 minutes

o SoilMapr will give a complete read of the nutritional qualities of the soil

o Input the crop to be cultivated

o SoilMapr will give a recommendation of nutrients that require additions, and specific fertilizers that could work

o SoilMapr would also predict expected yield based on current conditions, versus improved conditions – allowing users to make a judgment on need for investment.

o Place the SoilMapr sensor in the ground for 30 minutes to effectively monitor soil quality throughout

o SoilMapr would give a read of the nutrient quality, and also expected yield at current levels

SoilMapr has three sensors:

Optical sensors: Measure infrared levels, organic matter, and moisture content
Electrochemical sensors: Measure ions, pH levels and nutrient mix
Mechanical sensors: Measure soil resistance and compaction

Data collected from these sensors is modelled against datasets collected over time of ideal crop production, optimum soil mix, expected yields, harvest time, etc. Analysis will focus on leveraging robust data from evolved agrarian systems in developed markets to developing countries where this data is inaccessible. For example, historical corn production in the USA with irrigation of X liters per day grown on soil with specific characteristics, can serve as a reference and benchmark for what Ethiopian production could be under similar or slightly different conditions. These ballpark figures would create predictability, and over time feed back into the dataset as reference for other production centers – establishing average performance metrics, as well as best-in-class.

Demonstration

With large cycle times in agriculture, SoilMapr may be able to effectively display the ability to measure soil quality in real time, but would need a long window to validate its predictive qualities. The ideal demonstration would involve working on two identical plots of land, one where conventional farming methods are used, and another where SoilMapr is used. This would allow results to be compared easily to identify the value SoilMapr brings.

The farmer could then leverage SoilMapr’s interface to understand (a) what nutrients need to be added to the soil to be effective for their intended crop, (b) predict expected yield based on those specific environmental factors.

Screenshot of SoilMapr’s potential interface >>>>

Source

http://www.thisisafricaonline.com/News/Closing-Africa-s-agricultural-yield-gap?ct=true

https://www.populationinstitute.org/resources/populationonline/issue/1/8/

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2873448

http://cropwatch.unl.edu/ssm/sensing

Wear – You are what you wear, and you wear what you are

April 12, 2017vsaigal

The problem

When it comes to apparel, there are adventurous and conservative shoppers. Adventurous shoppers spend hours researching, browsing, experimenting, and trying on different styles of fashion, and have fun doing it. Conservative shoppers just want to occasionally purchase slightly different hues of the clothes they have worn for years, and the idea of shopping fills them with dread. They like to stick with the familiar because they often cannot visualize how different styles of clothes can look. The problem is often not that these shoppers are unwilling to wear different apparel, but that they are unwilling to put in the search costs. The unwillingness to experiment constrains the growth the $225bn apparel market and contributes to a fashionably duller world.

Our wardrobes for example

The solution

The team proposes an augmented judgment system that uses a shopper’s non-apparel preferences to predict apparel preferences that they may not be aware of. For example, if [Wear] knows that a shopper likes James Bond movies, enjoys wine tastings, drives a Mercedes, spends 45 minutes every morning for personal grooming, and prefers to eat at Grace, it would predict the type of apparel the shopper would like through a model that connects non-apparel preferences toapparel tastes. [Wear] would then remove apparel styles already owned by the shopper from the output to form a set of recommendations of new styles the shopper may like. The result would be distinct from that produced for a shopper who likes Mission Impossible movies, enjoys dive bars, rides a Harley Davidson, spends 5 minutes on personal grooming, and prefers to eat at Smoque BBQ. This model will use augmented perception techniques to understand different parts of the user’s environment that provide insight into their preferences. This algorithm will generate apparel recommendations that fit the needs and preferences of shoppers who may not understand their own preferences, leading to higher consumer willingness to purchase new apparel and higher industry sales.

The demonstration

The team would like to produce a working prototype of the system to illustrate its effectiveness in real time. The team envisions asking an audience member to fill out a basic survey on non-apparel preferences. The audience member would then be asked to predict a shirt he would like the most from a selection of 10 shirts, while the [Wear] algorithm will simultaneously predict his preferences. The person would then be presented with both shirts to try on and provide feedback on which one he liked more.

Sources:

https://www.statista.com/topics/965/apparel-market-in-the-us/

https://my.pitchbook.com/#page/profile_522553643

Uptake!

April 7, 2017April 7, 2017vsaigal

Uptake, a Chicago-based data analytic firm was founded in 2014 by Brad Keywell and Eric Lefkofsky to develop locomotive-related predictive diagnostics. Its predictive analytics Software-as-a-Service platform aims to assist enterprises improve productivity, reliability and safety through the suite of solutions including predictive diagnostics and fleet management applications.

Every time a piece of equipment goes idle due to equipment failure or poor planning there are two costs: a) the cost of the repair in parts, labor, etc. and, b) the opportunity cost of lost revenue. There are also substantial costs involved with keeping contractors nearby while waiting for the machines to return to service. Downtime, scheduled or unscheduled, is essentially time that the site and the equipment is not earning back its investment costs.

Uptake platform uses machine learning combined with knowledge from industrial partners to deliver industry-specific platforms and applications to solve complex and relevant industrial problems like predicting equipment failure to result in enormous savings. It combines data science with massive data generated by plethora of sensors in these machines to understand signals and patterns that can develop predictive diagnostics. In addition, to shifting from a reactive ‘repair after failure’ mode to a proactive ‘repair before failure’ stance, Uptake also helps customers track fuel efficiency, idle time, location and other machine data.

Uptake has a very strong value proposition and commercial relevance. The company claims that its solution covers industry segments including rail, mining, agriculture, construction, energy, aerospace and retail. Its marquee client is Caterpillar which has also invested in the firm. Instead of building its own integrated services, Caterpillar shared all the know-how of its equipment and works with Uptake, which has more than 300 engineers, data scientists, and designers. Uptake has also, recently publicly announced its foray in the wind energy space by adding added two subsidiaries of Berkshire Hathaway Energy to its client roster: MidAmerican Energy Company and BHE Renewables Uptake’s current annual revenue run-rate exceeds $100 million and because of its unique algorithm and industry focus its valued at $2Bn.

While, Uptake generates immense value for construction equipment predictive diagnostics, it can further improve the prediction by also incorporating the environmental conditions like soil structure, site geometry, operating weather conditions, precipitation in air, etc. Through the use of sensors, these factors can be predicted even before the equipment is put to use and can thus, help in better estimating the wear and tear costs and time delays associated with a given project implementation. Using this “perceptive” data collected through sensors, the equipment firms can also manage their replacement inventory, thus further reducing the operational costs.

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