Blue River Technology – Solution Profile (Shallow Blue)

Maker of See & Spray and LettuceBot

Outline of the Problem

See & Spray

At large farms, it is standard practice to spray an entire field evenly when a weed or pest problem emerges.  This uniform treatment of the crop can result in excessive use of pesticide and herbicide.  There are also environmental and food contamination concerns regarding the use of these pesticides and herbicides. The goal of See & Spray is to decrease the amount of chemical use in agriculture.

Blue River Technology (BRT) developed agricultural machines that utilize machine learning to distinguish between weeds and plants based on their size, shape, and color as the machines drive over fields.  The machines spray the chemicals in the exact spots they are needed, preventing chemical overuse. The robotics technology allows the smart machine to precisely spray herbicide on the field.

LettuceBot

At times, farmers tend to over plant certain crops, and so will thin out the crop to improve overall yield. Thinning out the crop is a labor-intensive and expensive process.

LettuceBot is a BRT machine-learning powered machine that is able to photograph 5,000 plants a minute, “using algorithms and machine vision to identify each sprout as lettuce or a weed.”  The plants can be identified by graphics chip in just .02 seconds. LettuceBot is also able to determine whether crops have been planted too close to each other, which could inhibit their growth.  If that is the case, it will spray and kill one of the plants without harming the other, increasing overall crop yield. This automates a normally labor-intensive process.

Evaluation of Effectiveness

BRT claims the See & Spray technology can decrease chemical use by a factor of 10. The accuracy of the machine is within a quarter of an inch. This can result in both cost savings to farmers in terms of fewer pesticides, and fewer environmental and food contamination concerns. However, most pesticides are fairly inexpensive, so cost savings are not huge, and we view this product as only moderately effective.

Given the normally manual nature of lettuce thinning, LettuceBot produces significant labor cost savings for farmers. LettuceBot is currently in use on 10% of the lettuce fields in the U.S., and this relatively wide adoption highlights how this product has been very successful so far.

Proposed Alterations

Below are six proposed alterations for BRT’s machines:

1. Improve specificity in identifying weeds

There are many different species of weeds that all have different optimal control agents, and weeds are starting to get more resilient; it is therefore becoming more important to identify exactly which weed is growing. BRT could incorporate multiple herbicides into its See & Spray machines and tailor them to the specific weeds identified.

2. Introduce nutrient spraying in addition to herbicide spraying

Given BRT’s existing technology, it should be fairly simple to incorporate targeted nutrient spraying for certain plants, such as plants that look small or weak. This would add an additional value proposition for farmers that does not already exist, as they could accomplish both tasks in one go.

3. Incorporate soil analysis into herbicide and nutrient spraying decision

The efficacy of certain herbicides can depend on the type of soil that the crops are growing in. Analyzing soil can allow for supplementation of nutrients for optimal crop growth.

4. Expansion of LettuceBot to other crop types

BRT should leverage its machine learning algorithms to teach its products to identify other plants, so its products can be used to thin multiple types of crops beyond just lettuce.

5. Market “Low-Pesticide” products

Given the recent popularity of organic foods, BRT should encourage their customers to promote the fact that their crops use 90% fewer pesticides. This would appeal to a health-conscious market and generate stronger sales for farmers, and thereby increase demand for BRT’s products.

6. Sell crop data to third parties

BRT’s products currently gather a wealth of data on the real-time quality of plants across all of its customers. This data could potentially be aggregated at a trend level and resold to financial groups (while protecting individual farmer anonymity) such as hedge funds that are trading agricultural futures.