Here’s a deep dive into those questions, and how DEM insights translate into better decisions, healthier orchards, and stronger returns.

If there are any more questions… I’ll see you in the comments!

1. How does “elevation blindness” affect decision‑making in orchard planning, and what are the long‑term economic implications?

Ignoring elevation data means missing hidden drivers of poor performance further down the line, from water‑logging and erosion risks to subtle soil‑related constraints that quietly limit tree health, yield, and long‑term orchard profitability.

A. Establishing New Orchards on Uncultivated Land

When planning a new orchard, topography is only part of the equation. Ignoring either elevation or soil type can set the project up for decades of reduced productivity.

A DEM gives you the slope, drainage, and watershed patterns, but it does not tell you what’s in the ground. In‑field soil sampling and classification are essential to confirm soil depth, texture, and drainage potential in different terrain zones.
For example:

• A gentle slope on the DEM may look ideal for planting, but field sampling could reveal shallow soils or a hardpan layer restricting root growth.

• Low‑lying areas might drain acceptably in sandy soils but could hold water and cause root asphyxiation in heavier clays.

• Subtle soil boundaries invisible in aerial data can influence root vigour, nutrient holding capacity, and susceptibility to compaction.

Combining DEM analysis with soil mapping allows for site‑specific block layout: selecting rootstocks, varieties, irrigation systems, and even tree spacing suited to each micro‑zone.

Skipping this step risks planting trees in unsuitable zones, leading to chronic underperformance and avoidable replant costs.

Here are some common terrain scan use cases:

• Hig-res orthomosaics

• Elevation mapping

• Drainage modelling

• 3D Terrain modelling

• Slope Modelling

When planning orchards (whether from scratch or in long‑standing blocks) terrain tells you how the water moves, but the soil tells you how the trees will live. Both must be read together to design for yield, longevity, and profitability.

B. Planning in Established Orchards

For mature orchards, elevation blindness can mask long‑term underperformance, but so can soil variability. Even in established blocks, soil surveys remain valuable - especially in conjunction with DEM data—because they help explain persistent problem zones.

For example:

• Depressions visible on the DEM may consistently hold water, but soil sampling can reveal whether that’s due to clayey subsoil, compacted layers, or organic matter accumulation.

• Erosion might thin topsoil in elevated zones, reducing nutrient availability and water‑holding capacity.

• Historical replant failures in specific spots could be linked to a combination of poor drainage (seen in DEM) and unfavourable soil chemistry or structure (seen in sampling results).

Integrating DEM layers with soil classification maps creates a more complete picture of orchard health and resilience. This enables targeted remedial actions like subsoiling, installing drains, applying organic matter, or altering irrigation scheduling to suit actual site conditions.

2. In what ways might using a DSM instead of a DTM lead to flawed interpretations in orchard terrain analysis?

A Digital Surface Model (DSM) includes tree canopies and structures.

• Useful for certain planning tasks, but it obscures the true ground surface.

  • For terrain‑related management (drainage, erosion, irrigation flow), the canopy height in a DSM can create false “high spots” and “low spots,” misleading decisions.

• A Digital Terrain Model (DTM) is essentially a “bare earth” DEM; which strips away vegetation to give accurate slope and elevation readings. DTMs frequently utilise LiDAR data, so if an unobscured view of the terrain surface is what you are looking for, aim for using a LiDAR scan.

3. How can DEMs be integrated into smallholder or resource‑limited orchard systems without heavy investment in technology?

While high‑resolution drone mapping can seem out of reach for smaller or resource‑limited operations, the reality is that DEM insights can still be brought into your planning without heavy capital investment.

The key is to think strategically about access rather than ownership.

You don’t need to run weekly drone flights to gain value; even an initial, well‑timed DEM survey can reveal drainage risks, highlight problem slopes, and guide infrastructure placement.

By pairing that elevation data with on‑the‑ground knowledge, on-the-ground soil observations (and basic mapping tools) smallholder farmers can prioritise the interventions that deliver the greatest return for the least cost.

Just as important as knowing the slope of your land is understanding what lies beneath it. DEMs can show where water will move and pool, but only soil sampling will confirm whether those areas can handle the moisture or are prone to water-logging, compaction, or nutrient leaching.

By combining elevation mapping with soil type verification, even a single well‑planned survey can give smallholders the confidence to adapt planting layouts, choose suitable rootstocks, and design irrigation systems that work with (not against) the natural character of the site.

Some quick ideas that might help:

• Partner with local drone service providers for periodic surveys rather than buying your own drone.

• Use free or low‑cost GIS viewers (ie. Aerobotics, or QGIS) to explore basic slope and contour maps, with nominal data processing fees - if any.

• Combine DEM data with simple field observations to focus limited resources on the most critical areas.

• Join co‑operative cost‑sharing programmes for annual surveys. Especially effective in high‑value crops, or where full-farm orchards hectares are below usual data-collection Minimum Order Quantities (MOQs).

As little as one good DEM can guide major improvements, and provide a base for extensive analysis or operational enhancements.

And as always, with AgTech solution connected to the cloud (for viewing data, and collaborating with consultants and/or team members) a stable internet connection will go a long way! I’m only mentioning this, as experience tells me that many smallholder operations are often found in hard-to-reach places, where internet coverage can be patchy.

4. What are the strengths and weaknesses of SfM photogrammetry vs LiDAR in generating DEMs for orchards?

To clarify for readers who are not familiar, Digital Elevation Models (DEMs) can be generated from RGB images using Structure-from-Motion (SfM) photogrammetry. SfM is a technique that uses overlapping RGB images to reconstruct a 3D model of a scene, which can then be processed into a DEM.

Choosing between Structure‑from‑Motion (SfM) photogrammetry and LiDAR isn’t just a technical decision - it’s about matching the tool to your orchard’s unique conditions and your operational goals.

Both methods can produce high‑quality Digital Elevation Models, but they do so in very different ways, each with its own cost, accuracy, and practicality considerations. Factors such as canopy density, row spacing, budget, and the level of detail you need will guide the choice.

Understanding where each technology excels (and where it falls short) helps ensure you’re collecting terrain data that’s truly fit for purpose, whether you’re planning new orchards, diagnosing drainage issues, or tracking landscape changes over time.

• SfM photogrammetry (from RGB images):

  • Affordable and accessible.

  • Works well in open orchards with clear ground visibility.

  • Struggles in dense canopy cover, and does not “see” under trees.

• LiDAR:

  • Penetrates canopy to capture bare‑earth terrain.

  • Higher accuracy in vegetated areas.

  • More expensive and requires specialised hardware.

For most commercial orchards with standard row spacing, high‑quality SfM photogrammetry is sufficient.

5. Drone‑derived vs satellite‑derived DEMs — which should I use?

When deciding between drone‑derived and satellite‑derived DEMs, the trade‑off comes down to resolution versus coverage.

Drones capture fine‑scale detail ideal for orchard‑level planning, while satellites offer broader regional context but lack the precision needed for tree‑level insights.

Data availability can also be a factor too. High‑resolution satellite DEMs may be restricted, outdated, or expensive; whereas drone data is comparably accessible, and can be captured exactly when and where you need it.

• Drone‑derived data:

  • Resolution: typically 3–10 cm.

  • Ideal for orchard‑level planning and in‑row diagnostics.

  • Captures micro‑variations that satellite data misses.

• Satellite‑derived:

  • Resolution: typically 5–30 m.

  • Useful for regional planning or large‑scale watershed analysis.

  • Not detailed enough for precision orchard work.

If you need to plan drainage for specific blocks or diagnose tree‑level stress, drones win every time.

6. How can DEMs proactively identify erosion risk before it’s visible?

Erosion rarely announces itself until damage is done! But DEMs can reveal the early warning signs. By analysing slope, drainage patterns, and terrain breaks, you can pinpoint areas where water movement is most likely to strip away topsoil - long before visible gullies or sediment loss occur.

Slope analysis from DEMs can reveal:

• Concentrated flow paths.

• Steep sections prone to runoff.

• Depressions where water slows, and area where topsoil erosion has already occurred.

By overlaying these with rainfall data and soil type maps, you can identify erosion “hot zones” before larger gullies form, or massive topsoil build up occurs where it shouldn’t.

7. What are the risks of neglecting DEM‑based analysis in infrastructure planning?

Infrastructure decisions set the framework for decades of orchard operations, and terrain plays a critical role in getting them right.

Without DEM‑based analysis, key assets like irrigation networks, roads, and storage facilities may be placed in locations that compromise efficiency, increase maintenance costs, or even create long‑term drainage and erosion problems that are costly to fix later.

• Poorly sited irrigation lines that lose pressure or flood low areas.

• Roads or tracks that channel water into orchards.

• Buildings or dams placed in spots prone to flooding or unstable slopes.

Once built, moving infrastructure is costly — getting it wrong at the start can lock in inefficiency for decades.

8. How do DEMs optimise Variable Rate Application (VRA)?

Variable Rate Application (VRA) is usually built around soil fertility data, crop vigour and stress maps, or yield history. While these are important, they only tell part of the story.

Elevation dictates how water and dissolved inputs actually move across the orchard. And ignoring this can undermine even the most carefully designed VRA program.

On sloping ground, fertilisers, soil amendments, or crop protection products can migrate downslope with rainfall or irrigation. Low‑lying areas may collect excess nutrients or chemicals, while higher elevations may remain under‑supplied. This leads to inefficient use of inputs, uneven growth, and in some cases, environmental run‑off concerns.

By integrating DEM layers into your VRA mapping, you can model slope, flow paths, and accumulation zones, then adjust application rates accordingly. This ensures each zone receives what it can retain and use effectively. For example:

• Reduce rates on slopes prone to leaching or run‑off.

• Increase rates in elevated sandy soils where infiltration is fast and retention is low.

• Modify application timing in depressions to avoid pooling or nutrient tie‑up.

The result is more precise targeting, better uptake, and reduced waste; with the added benefit of improved orchard uniformity over time.

Adding terrain information is an additional factor to consider, in terms of VRA strategy, but the longterm results far outweigh the efforts when done right.

9. Can DEMs predict stress zones when combined with NDVI or NDRE?

Absolutely. And the real power comes when you layer multiple metrics together for a more complete diagnosis.

Elevation alone can point to likely stress drivers, but pairing it with vegetation indices such as NDVI or NDRE confirms whether those terrain patterns are actually impacting plant health and vigour.

Elevation‑linked stress is a common theme in perennial crops:

Low zones → prone to excess moisture, poor drainage, oxygen‑limited root zones, nutrient leaching, and in some soils, heightened disease pressure.

High zones → more exposed to wind and solar load, with quicker moisture loss (quicker infiltration, evaporation, or low organic matter soils, etc) and greater drought stress risk.

When NDVI or NDRE maps highlight elevated stress in patterns that match terrain‑defined zones, you have a strong case for terrain‑linked intervention. But it doesn’t stop there.

From other data layers often readily available:

• Thermal (Transpiration Uniformity): Low‑lying waterlogged areas often show low canopy temperatures due to reduced transpiration efficiency. Conversely, high‑elevation drought‑stressed trees may also run hotter.

• Canopy Metrics: DEM‑linked stress zones often show smaller canopy area or reduced indicative vegetative volumes - a strong visual cue of chronic underperformance.

• Historical Change Detection: Comparing DEM‑linked stress zones across seasons helps separate one‑off seasonal effects from persistent structural problems.

By integrating DEMs, NDVI/NDRE, thermal, and canopy data, you’re no longer just identifying “where” stress is happening… you’re understanding why it’s happening. That makes your in‑field scouting more targeted, your remediation work more effective, and your long‑term orchard planning more resilient.

10. What spatial resolution is “good enough” for orchard analysis?

• 3–10 cm: Ideal for tree‑level diagnostics and in‑row slope analysis.

• <20 cm: Acceptable for block‑level planning and drainage mapping.

• >50 cm: Too coarse for most precision orchard uses.

The tighter the resolution, the more confidently you can act = improved ROI.

11. How does DEM data influence financial decision‑making?

• Irrigation investment: Direct funds to areas with the greatest water distribution challenges, ensuring capital is spent where it delivers the most benefit.

• Variety selection: Match cultivars to terrain‑specific stress patterns, reducing long‑term losses from unsuitable plantings.

• Infrastructure placement: Save money on rework by getting it right the first time — from roads to dams to pump stations.

• Opportunity cost: Avoid tying up land, capital, and management time in underperforming zones by identifying them early and planning accordingly.

• Labour and machinery efficiencies: Plan block layouts, access routes, and work sequences that minimise travel time, fuel use, and unnecessary equipment wear, reducing both input costs and wasted hours.

• Drainage works: Prevent costly re‑installation by using DEMs to design systems correctly the first time, avoiding repeated excavation, material, and labour costs.

“Every drainage decision you get wrong today is a cost you’ll pay for twice — first in installation, and again in years of lost yield.” - Johnnie van der Walt, Precision Drainage

Better terrain insight reduces guesswork - and that directly affects ROI. Over the lifetime of an orchard, those savings compound into a significant competitive advantage.

12. How can DEM analysis be made more accessible in regions like Uganda or East Africa?

I understand that in many parts of East Africa, the challenge isn’t the value of DEM data - it’s access. By lowering the cost of collection and building local capacity to interpret terrain insights, even smallholder farmers can use elevation data to guide smarter, more resilient decisions.

• Build and facilitate training around interpreting DEMs.

• Offer shared drone services via co‑ops, or hardware share agreements.

• Integrate DEM data into affordable mobile platforms for easier farmer access.

• Partner with NGOs focused on climate resilience, sustainability, and food security.

Remember, digital is the great leveller when it comes to knowledge and solution access. Once the hardware solution is solved (data collection) it get’s easier from there on out. Then it needs to be assured that education on data use is not forgotten.

13. How can farmers be trained to interpret DEMs effectively?

• Utilise available resources. Online info, courses, books, videos, etc.

• Practical workshops using local case studies, and in-person meetings.

• Side‑by‑side comparisons of RGB vs DEM outputs (ideally other metrics too).

• Linking DEM patterns directly to in‑field observations and yield outcomes.

• Practice through exposure - taking action!

• Then, Intervention tracking, and continual feedback loops (which lead to additional talking points at practical workshops and think-tanks).

• Follow‑up mentoring to embed the skills.

Final Thoughts

DEMs aren’t just “extra data.” They’re the missing terrain context that lies beside and together with every other orchard metric. From canopy growth to nutrient use. Ignore their utility at your own peril, and risk farming blind to one of the most influential factors shaping yield.

Thanks again for the questions, and enquiries - I appreciate you all.

Leave a comment

The DJI Mavic 3M Multispectral Edition (also known as the DJI M3M) pairs an RGB camera with a multispectral camera. This combination provides a detailed view of crop health status.

Believe it or not, this powerful data-collection combo has been a tactic employed by companies like Aerobotics for more than a decade.

Why focus on the Mavic M3M specifically?

The barrier to entry for aspiring drone pilots and farmers alike, has traditionally been quite high. The cost of equipment and hardware availability, a major part of the challenge.

The DJI M3M setup is a lot smaller, as well as more affordable to procure and maintain. Keeping this in mind... the high-value data these types of surveys produce has become more accessible than ever before. It’s quite literally a game-changer. A drone-tech adoption accelerator enabling drone service providers, software companies, and the end-users (farmers) alike.

The best part? Multiple drone metric outputs at your disposal with split-screen views. Especially useful when it comes to the analysis of the survey results. Diagnosing of potential issues and anomalies, and setting up next-steps for interventions.

In the case of the Aerobotics platform, the multispectral values can be displayed as either NDVI or NDRE metrics, and in three view options:

1. Map layers

2. Zonal views

3. Per-plant metrics

Multispectral information helps us to spot high stress or low health issues early on. Way earlier than one would do otherwise with the naked eye. The bird's-eye-view gained from a drone overhead makes for high-efficiency top-down screening.

And over and above the split-screen views comparing metrics... one can review survey data; comparing results across drone survey dates as well. Great for tracking changes over time.

Additionally, one can setup tasks for in-field inspections (mobile app integration). Share information via in-platform chats and discussions (creating a diagnostic archive). Export data results for offline scrutiny or sharing online (optional).

And affordability? When compared to larger setups like the DJI Matrice 350 RTK paired with 3rd party payload sensors, the M3M setup comes in at only a fraction of the cost.

With a 20-megapixel RGB camera with a four-thirds CMOS sensor, the image quality is superb for a drone and camera this size, getting the job done. While RTK positioning delivers centimetre-level accuracy, synchronised to the microsecond.

Every camera exposure is precisely recorded, efficiently and quickly. This allows the drone to survey up to 200 hectares in one flight, without ground control points.

It’s not unheard of for an experienced drone pilot flying in favourable weather, being able to survey around 350 hectares in a single outing (with a couple of battery changes of course).

In this way, farmers are able to procure high-quality datasets with ease. High-res visuals and multispectral information as standard. But also canopy sizes, elevation, and tree census statistics too. Info from each drone survey optimises key operations. Activities like spraying, fertilising, irrigation, and VRAs.

Of course, plant growth and sizes of plants vary within a field (ie. orchard variance) making targeted (or variable) fertilisation essential nowadays.

Using advanced AI-backed platforms, farmers can generate vegetation indexes. These highlight differences in crop size, growth, stress, and vigour.

Once again, these vegetation insights form part of the puzzle for creating precise VRA prescription maps. Cutting overall costs and reducing environmental impact, while maximising yield potential.

The M3M absolutely excels in crop scouting. Images are uploaded in real time via a 4G network to the DJI Smart Farm platform. Alternatively, straight to the Aerobotics platform post-survey via WiFi connection for automated processing. Expertly built AI-backed software, with state-of-the-art analysis tools and diagnostic solutions.

These cloud-based platforms enable quick identification of areas under undue stress. Including outliers and anomalies; quantifying the magnitude of the problem o rpattern of concern.

As mentioned, cloud-based sharing makes collaboration seamless. And the platform’s AI tools assist with yield monitoring and strategic planning. Even larger farms can be managed efficiently by a single operator. In turn, high quality data becomes a lever for taking action. An advantage enjoyed by heads of operation, production managers, and agronomists alike.

Beyond standard tree crops, the Mavic 3M is useful for surveying cash crops. maize, wheat, cotton, potatoes, and more. Equipped with DJI O3 transmission, it can cover vast areas, including mountains, forests. But most importantly: orchards and vineyards.

A downward-facing vision sensor helps it maintain altitude over uneven terrain, improving efficiency. Through the Aerobotics platform, it can count trees (and gaps in rows) and display differences in ground-cover.

The M3M can even generate 3D flight routes for other agricultural drones. Making spray application by drone even more precise, and safer than ever.

The Mavic 3M is built to boost efficiency and sustainability in farming. It enhances productivity, while reducing costs. Minimises environmental impact. And makes our modern era of agriculture smarter and more effective.

Hats off to DJI for an amazing piece of kit.

In Summary:

• The smaller compact design compared to other multispectral setups makes for unsurpassed utility, convenience, and affordability.

• The M3M combines RGB and multispectral cameras for detailed analysis.

• RTK positioning ensures high accuracy, allowing efficient surveys without ground control points.

• Terrain-following sensors improve efficiency in challenging landscapes.

• AI-powered software aids in identifying plant health issues and crop risks quickly.

• Integration with DJI Smart Farm enables general mapping, and helps to generate drone spraying programs.

When data is uploaded to the Aerobotics platform this allows for expert analysis, access to first-in-class cloud-based solutions, task setting, collaboration, knowledge archiving, integration with scouting tools like advanced mobile apps, and VRA map generation (to name a few).

» Mapping a Field with a Multispectral Drone | DJI Mavic 3M

» DJI Mavic 3 Enterprise Multispectral Comprehensive Review

» DJI Mini 3 Pro Preflight Checklist: Thing To Do Before You Fly

Not to mention challenges like extreme weather, biodiversity loss, and soil depletion.

The current situation has forced us to reassess farming tactics in many parts of the world.

Drones and AI are playing a crucial role in making cultivation more sustainable. Providing farmers with data to improve efficiencies. Reducing energy consumption, and reducing costly chemical inputs. All while maintaining harvest sizes, and most of all, securing incomes.

The mountain ahead is the need to prioritise regenerative and sustainable farming techniques. Those that enhance soil health, reduce chemical inputs, and promote biodiversity.

This approach will have direct and indirect implications for energy efficiency.

This lofty goal is crucial. It will also develop a systematic habit of lowering carbon emissions. While maintaining focus on food security and quality - which remains essential.

It's well known that one key area requiring change is soil management. Heavy machinery is compacting arable land, significantly hindering crop yields.

Large-scale agriculture still remains reliant on tractors and other heavy equipment. These exert immense pressure on the soil as they traverse fields. This agri-traffic causes the soil to become densely packed and compressed.

This compaction reduces the space between soil particles. Limiting root penetration, while excluding oxygen. Hindering the development of a healthy root systems. Without sufficient room to access water, air, and therefore nutrients... crops struggle to thrive.

Additionally, compacted soil has a reduced ability to actually retain moisture. Spaces between particles are smaller; the ground less porous. Making it more vulnerable to drought stress.

Compaction will also impair drainage, increasing its susceptibility to water-logging during wet periods. Compacted soils have low infiltration rates. Water stands without permeating downwards as it otherwise would.

This water-logging accelerates the spread of pathogens. Roots drown. It's a common cause of plant health decline. A condition that leads to significant yield losses. And losses that become a long-term problem in the affected area. Rendering fields economically unsustainable to manage. The plants are simply not paying for themselves compared to the input costs per season.

To address these challenges, farmers are improving their practices:

• Limiting in-field traffic, and using lighter machinery.

• Employing wider, lower-pressure tyres to reduce soil compaction.

• Making use of tech-forward and innovative solutions.

Innovations include sophisticated in-field telemetry, remote-sensing technologies and cloud-based platforms. Together these detect, monitor, quantify, and deliver insights on soil compaction severity.

To go a step further, farmers pair insights with mobile applications. These allow for efficient data capture in-field. Actions such as inspections or recording intervention steps. AI-backed mobile applications are also enhancing in-field sampling methods.

“Modern 4IR tools provide farmers with critical data to make informed decisions. Pivotal to promoting healthier crop development and lowering variance across and within fields. An important step towards increasingly sustainable farming practices.”

Furthermore, drone technology is being applied to optimise orchard performance, while minimising in-field traffic, or simply to cover more ground more effectively in terms of monitoring fields.

Drone us of course includes chemical spraying, insect baiting, cover crop seeding, and beneficial insect releases too. And tree health detection is an impactful exercise, as is conducting a plant census survey.

Insurance-related surveys, growth progress reporting, and surveys done after severe weather, add further value to the drone use-case.

Outside of applying chemicals, conducting health scans, or conducting surveys for insurance purposes, drones are also enhancing irrigation systems management.

Water management is another crucial aspect of sustainable farming. Precise water use has one of the most significant impacts on crop health. Particularly when nutrients, both organic and synthetic are delivered through increasingly sophisticated irrigation systems.

Well-managed irrigation practices account for specific crop requirements. While climate data, in-soil sensors, and remote sensing technologies empower tailored nutrition programmes.

Utilising thermal sensors and AI, farmers are detecting anomalies and quantifying orchard variance. Identifying inefficiencies and their likely causes without requiring extra in-field traffic.

Data on transpiration uniformity and plant stress enables farmers to reduce or replace costly traditional inputs. This includes energy use, ultimately lowering agricultural emissions in the process.

An Aerobotics case study (Duck River Farm, Portugal) highlights how drones are revolutionising precision agriculture:

Can you imagine cultivating almonds across 3,000 hectares? Monitoring each tree in detail becomes a real challenge. Especially when attempting to identify underperforming trees affected by water shortages.

However, Duck River (using high-resolution thermal imaging via drone flight) detected trees with low transpiration rates. These correlated with poor canopy health.

Investigations revealed a misplaced drip line. This line alone was impacting 125 trees, and 18 ruptured lines were affecting 825 trees. These discoveries safeguarded trees with a lifetime revenue exceeding €90,000 (± $94,000).

Over and above insights that fuel efficient water use, it's clear that drone surveys also provide comprehensive plant health and canopy status statistics. This information allows production managers to prioritise interventions. With less of a focus on a blanket approach to crop management.

Drone-derived insights are then integrated with sustainable farming practices. Such as reduced tillage, and indication of areas requiring additional organic matter, or well-managed cover cropping. Techniques recognised as vital for maintaining soil health.

Additionally, reducing in-field traffic and regular tillage lowers the carbon footprint of farming. In effect, decreasing reliance on fossil fuels which still power most agricultural machinery.

Given the energy intensiveness of traditional farming techniques, modern agriculture must explore more sustainable and efficient alternatives.

Remote sensing and in-field AgTech that works in harmony with nature is of paramount importance. Helping us to tread lighter, work efficiently, and manage resources effectively. Building sustainability into the DNA of the farming operation.

Technologies such as satellites and in-field telemetry, provide farmers with a clearer view of problem areas and changes in-field. Allowing them to apply inputs more precisely.

The elephant in the room is the overuse of chemicals for plant growth and eliminating pests, and this remains a significant challenge.

Traditional inorganic fertilisers are produced through energy-intensive processes. Extracting raw materials (natural gas, phosphate rock, etc) and converting them into plant-available compounds.

These inputs are not only costly, time and energy-intensive, but moreover have a negative impact on biodiversity on-farm, and the environment as a whole.

But yes. Sustainable alternatives are gaining traction. Variable Rate Applications (VRA) use is fast becoming the norm. A relatively easy go-to for AgTech newbies with a comparatively low barrier to entry.

VRA can be as basic or complicated as the operation wants to make it: From manual granular applications (guided by drone data metrics) to automated granular spreading - like that done with a Red Ant machine - there are a lot of options.

Calibrated bio-spreading of compost mulch, based of zonal drone-derived metrics. Spot spraying by knapsack, or by drone. Or fully autonomous GPS-guided rigs like those from GUSS. The options are numerous when it comes to VRA, and there’s a place for at least some kind of variable application method for most farming operations.

Above: Spreading compost mulch variably according to zonal outputs after a recent drone survey as part of a structured orchard regeneration plan. Increasing general tree health, building healthy soil, ensuring sustainable yields.

Another Aerobotics case study illustrates the benefits of drone data-backed VRA applications:

Kriegler Farms in the Western Cape, South Africa grows premium table grapes. Fertilisers used to be applied uniformly - the same amount everywhere. But this was soon to change.

Using high-res drone imagery and AI, vine health variability across vineyards was established. Precise nutritional programmes developed. And systems put in place to apply fertiliser only where needed.

The VRA approach reduced fertiliser and ameliorant inputs by 75,367 pounds (34,186 kilograms). While increasing yields by 9% and boosting revenue by $125,595.

With VRA strategies in place, production teams are reducing inputs and stretching their available budgets. The upside of reduced chemical use, and reducing traffic in-field is an added bonus.

Furthermore, these types of practices have positive ripple effects across the farming environment. Crop health, soil health health, and operational sustainability included.

Drone flight comparisons (across surveys and metrics) show how effective interventions have been. Data interpretations and monitoring changes over time aids in managing agricultural inputs. Changes are often quantified and results objective.

Drone technologies (combined with in-field mobile apps) flag concerns and quantify infestation severity. Right down to the number of affected plants and the urgency of intervention/s required.

A next step includes deploying beneficial insects in targeted areas using solutions like SkyBugs by Aerobotics. Or identifying release area with precision for hand-releases.

Furthermore, online platforms with “smart sampling” capabilities combine cloud-based technology with mobile applications. Allowing farmers to sample from representative areas, problem hotspots, or variances in yield.

Yield sampling itself is a contentious topic. Smart sampling techniques such as those that leverage AI, are removing doubts.

AI-generated sample points are helping to improve data integrity and representativeness. Modern techniques such as these lower the margin of error when it comes to yield forecasting.

As precision agriculture continues to evolve... the integration of drone technology, AI platforms, and sustainable farming practices will form part of the broader solution. An answer to shaping a more resilient and environmentally responsible way of producing food.

Smart technologies like those of new age mobile app’s are transforming the agricultural landscape. Drones are continuing to play a larger role in farming with each passing season. Replacing doubt with objective data results.

Cloud-based platforms also allow data to be processed off-site, and reviewed from afar. Consultants and experts can weigh-in with advice from anywhere in the world.

Asynchronous global companies are sharing notes, results, and strategies with ease. The farming world has never been more connected. Knowledge sharing has therefore gained a new kind of momentum; in a new age of farming.

Let’s work with it as best we can!

Not focused on any single crop type, but more an overview of Big Data technologies, our article is a quick-view of the tools, benefits, synergies, challenges, and opportunities for Big Data use in Agriculture.

We touch on working with data, weather stations, in-field telemetry, remote sensing, yield predictions, sustainability, and more. Well rounded, but still a quick read at 4 or so pages. A good tribute to 4IR and agriculture.

Read the full article:
How Big Data is Driving Transformation in Agriculture (AgriAbout.com)

We focus on using data for perennial orchards like fruit and nut farming, vineyards, and berries. However, no matter what you farm - if you grow something in the ground - you will find value in this free short-course.

A recurring remark from clients in my line of work has been, “I don’t understand or know much about the metrics I can use on my farm once I’ve had a drone survey.”

And so the aim of creating a week's-worth of study material in email format is to provide readers with an easy-to-digest yet high-impact course about drone derived data metrics for orchard farming.

The more we learn about the details of AgTech and the nuances of Precision Agriculture, the more we can take advantage of the tech and the opportunities on offer in the modern age of agriculture.

Many of the lessons come with in-text links for the curious of mind, and links at the end of sections for further reading - totally optional, but available and accessible.

Below is a quick breakdown of the course. There’s a little more than what you see here, and I’ve added as much value as possible into the body text of each email.

Lesson 1 - Introduction

• PDF eBook download - a supportive resource to the course

• Drones in agriculture - an overview

• Popular drone data metrics used in Precision Ag - an overview

Lesson 2 - RGB Imagery

• All about visual remote sensing imagery

• Bonus: Data use cases

Lesson 3 - Digital Elevation Models

• What are DEMs?

• Main types used in Agriculture

• Practical tips & use cases

Lesson 4: Multispectral Data

• Multispectral Introduction

• NDVI and NDRE

• What's the difference?

• Practical tips & use cases

Lesson 5: Plant Census

• Plant Census basics

• AI Plant Detection

• Inventory Management

Lesson 6: Canopy Data

• Canopy area

• Vegetative volume

• Variable Rate Application use cases

Lesson 7: Thermal info

• Thermal map layers

• Transpiration representations

• Data-guided irrigation management tips

Lesson 8: Bonus Email

• Notes on AI Smart Sampling

• Course conclusion

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These insects are all round amazing. And they’ve been part of Integrated Pest Management (IPM) solutions for a while now, but things just got a whole lot more interesting.

Traditionally, these clever critters are released by hand from tubes or other containers that house them. They crawl out and disperse from these containers and each beneficial insect has a general search radius - how far it will move in search of food, in essence hunting for its prey.

So distribution of these insects is really important. Using scouting results and looking at hotspot areas can work well, and this is a great approach.

However, this can be time consuming on both the data collection side (where are these pests?) as well as the actual placement of beneficial insect containers. There are labour costs involved, and of course the opportunity cost of human capital engaged in time-consuming insect releases on foot where human resources could be used elsewhere on-farm.

Introducing SkyBugs

A new Insect release method for farmers - dropping beneficial bugs over orchards as part of an advanced IPM strategy.

Currently available in South Africa, and powered by Aerobotics and fieldBUGS.

This DroneTech solution looks promising with fantastic initial results and sterling client reviews so far, at least for those ahead of the curve and taking advantage.

Farmers already taking advantage of this method of insect release are reaping the rewards, particularly for the control of Mealybug, where biological pest control can be administered quickly and easily and in the fraction of the time it usually takes a team to disperse the beneficials by hand.

The SkyBugs method ensures accurate release of beneficial insects on designated blocks, and with really quick turnaround times for the farmer.
- Monique Heydenrych (certified SkyBugs insect release drone partner)

Beneficial insects currently available for SkyBugs releases:

• Perminutus

• Anagyrus

• Lacewing

Focus crops currently include:

• Citrus

• Avocado

• Macadamia

• Mango

• Wine Grapes

• Table Grapes

• Stone fruit

• Blueberries

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