The below article was written and shared by Monique Heydenrych, the Port Elizabeth branch manager of Pretoria based South African aerial data capture enterprise Drone Solutions International.

Hey­denrych is an agri­cul­tural spe­cial­ist and doubles as the head of sales at DSI, and here she shares her know­ledge on the oper­a­tional land­scape of drone-based pest and dis­ease detec­tion, examin­ing the hard­ware, sensors, detect­ab­il­ity of spe­cific threats, and the prac­tic­al­it­ies of inter­pret­ing the data.

As she explains, the agri­cul­ture industry the world over is under­go­ing a quiet yet rapid revolu­tion; not in the field or the orch­ard but in the sky. For gen­er­a­tions, farm­ers have relied on manual scout­ing – walk­ing rows, check­ing leaves, and trust­ing gut instinct – to mon­itor crop health.

However, manual scout­ing is labour-intens­ive, time-con­sum­ing, and prone to human error, often identi­fy­ing prob­lems only after they have spread sig­ni­fic­antly.

This status quo has persisted for so long in the industry that English language purists have expanded the meaning of the word agricultural to refer to any activity that simplistic, straight-forward and is a marvel in its lack of sophistication.

Today, unmanned aer­ial vehicles, com­monly known as drones, offer an ini­ti­at­ive-tak­ing solu­tion. By cap­tur­ing high-res­ol­u­tion data from above, drones reveal the invis­ible, allow­ing grow­ers to detect early signs of stress, optim­ise inputs, and secure yields.

 The right drone for the right job

“Select­ing the cor­rect drone is the first step in suc­cess­ful aer­ial sur­vey­ing,” Heydenrych explains.

“The choice depends on the ter­rain, the crop type, and the scale of the oper­a­tion.”

She explains further that there are three primary cat­egor­ies of drones cur­rently dom­in­at­ing the agri­cul­ture sec­tor:

·         Multi-rotor Drones: These are the work­horses of pre­ci­sion agri­cul­ture and include quad­copters and hex­a­copters.

·         Known for their man­oeuv­rab­il­ity and abil­ity to hover, they are ideal for smal­ler blocks, com­plex orch­ard ter­rain, and detailed inspec­tions. Their ver­tical take-off and land­ing (VTOL) cap­ab­il­ity allows them to oper­ate in con­fined spaces without the need for a run­way. And because they can main­tain con­sist­ent alti­tude and speed, they are cru­cial for accur­ate data col­lec­tion in high-value crops like mac­ad­amias and cit­rus.

·         Fixed-wing Drones: Resem­bling small air­planes or gliders, fixed-wing drones are designed for effi­ciency over large dis­tances.They util­ise lift, gen­er­ated by wings rather than rotors, allow­ing for longer flight times and higher speeds. They are best suited for broad-acre mon­it­or­ing and regional sur­veys, cov­er­ing hun­dreds of hec­tares effi­ciently.

·         VTOL Hybrid Drones: A grow­ing cat­egory of drones involves hybrid options that com­bine the take-off flex­ib­il­ity of a mul­tiro­tor with the range of a fixed-wing. 

These offer the ver­sat­il­ity required for vari­ous agri­cul­tural tasks, bridging the gap between detailed inspec­tion and large-scale map­ping.

Beyond the naked eye

Hey­denrych notes that the drone itself is merely a car­rier; the real power lies in its pay­load. Mod­ern agri­cul­tural drones util­ise a soph­ist­ic­ated array of sensors to ‘see’ bey­ond the cap­ab­il­it­ies of the human eye.

She explains the tech­no­logy behind the drones as fol­lows:

• RGB Cam­eras: Stand­ard high-defin­i­tion cam­eras that cap­ture ‘true-col­our’ imagery using red, green, and blue light (the same range as the human visual spec­trum). While it is basic, RGB imagery provides a highresol­u­tion bird’s eye view that reveals pat­terns that are undetect­able at ground level, such as dis­col­our­a­tion, can­opy gaps, and zones of decline. Through tech­niques like ste­reo­pho­to­gram­metry, RGB data is also used to gen­er­ate digital elev­a­tion mod­els. These 3D rep­res­ent­a­tions of the land­scape help farm­ers under­stand how ter­rain, slope, and drain­age influ­ence dis­ease pres­sure, such as root rot in low-lying water­logged areas.
• Mult­isp­ectral Sensors: Multis­pec­tral ima­ging is the corner­stone of the early detec­tion of stress. These sensors meas­ure light reflect­ance in spe­cific bands, includ­ing near-infrared (NIR) and red edge, which the human eye can­not pick up. Healthy plants strongly reflect NIR light, while stressed plants do not. By cal­cu­lat­ing veget­a­tion indexes, these sensors reveal physiolo­gical stress days or weeks before vis­ible symp­toms appear. The Nor­m­al­ised Dif­fer­ence Veget­a­tion Index (NDVI) meas­ures gen­eral plant vigour. It is best suited for younger trees, cover crops, or crops with smal­ler can­op­ies. The Nor­m­al­ised Dif­fer­ence Red Edge (NDRE) veget­a­tion index uses the red edge band to pen­et­rate deeper into can­op­ies. It is essen­tial for detect­ing early stress in mature, dense orch­ards where the NDVI might give false ‘healthy’ read­ings.
•  Thermal Sensors: Thermal cam­eras detect infrared radi­ation, trans­lat­ing heat into vis­ible maps. This is vital for mon­it­or­ing tran­spir­a­tion, the pro­cess through which plants release water vapour through tiny pores called sto­mata. Healthy trees tran­spire and stay cool, whereas stressed or dis­eased ones often close their sto­mata, lead­ing to higher leaf tem­per­at­ures. Thermal sensors detect these ‘hot spots’, often indic­ated as tran­spir­a­tion uni­form­ity or tran­spir­a­tion vari­ance on data plat­forms. High thermal vari­ance can pin­point irrig­a­tion fail­ures, soil com­pac­tion, or root dis­eases like Phytoph­thora.

 Detecting the invisible: Diseases and pests

“One of the most fre­quent ques­tions farm­ers ask is, ‘Can the drone see the bug?’” says Hey­denrych.

“Not exactly. Think of it this way: the drone can­not spot a single pest or a tiny germ. What it does is spot the symp­toms; the subtle changes in the plant’s health that show it is under attack,” she explains.

She adds that advanced drone-based tech­no­lo­gies, util­ising thermal, RGB, and multis­pec­tral sensors, are highly effect­ive at dia­gnos­ing and pre­cisely map­ping pat­terns of physiolo­gical stress caused by a wide range of pests, such as army­worms and borers, and dis­eases, includ­ing fungal, bac­terial, and viral infec­tions.

“[The drone’s] primary role is to loc­ate and flag anom­alies. The cru­cial pro­cess of ground-truth­ing by human scouts remains indis­pens­able for defin­it­ively identi­fy­ing the spe­cific caus­at­ive pest, patho­gen, or defi­ciency to ensure the selec­tion and imple­ment­a­tion of the most accur­ate and effect­ive man­age­ment strategy.”

How drone distinguish between issues

If a drone detects plant stress, how can the farmer tell if it’s due to insects, dis­ease, or a lack of water?

“This dis­tinc­tion requires a com­bin­a­tion of spec­tral ana­lysis, arti­fi­cial intel­li­gence [AI], and ground-truth­ing,” explains Heydenrych.

The options for invest­ig­a­tion are through spectoral and thermal signatures, smart sampling, as well as AI and machine learning

Sp­ectral and Thermal Sig­na­tures: Dif­fer­ent stressors affect plants dif­fer­ently. A fungal infec­tion can reduce chloro­phyll, caus­ing a spe­cific shift in the red-edge spec­tral band, whereas drought stress might cause a dif­fer­ent spec­tral shift long with a uni­form increase in can­opy tem­per­at­ure.

AI and Machine Learn­ing: This is the gamechanger in mod­ern agricultural technology. AI plat­forms ingest vast amounts of drone imagery and learn to recog­nise spe­cific pat­terns. AI can dis­tin­guish cir­cu­lar patches caused by fungal infec­tions from the lin­ear dam­age of pest infest­a­tions that fol­low crop rows.

“There are algorithms that can now assign ‘risk scores’ or prob­ab­il­it­ies to spe­cific zones,” says Hey­denrych.

Smart Sampling (ground-truth­ing): Des­pite these advance­ments, AI acts as a triage tool rather than a final dia­gnosis.

The drone data gen­er­ates a ‘scout­ing route’, guid­ing the farmer to spe­cific trees or zones that rep­res­ent out­liers or high vari­ance.

This ‘smart sampling’ ensures farm­ers spend their time inspect­ing the most crit­ical 5% to 10% of the orch­ard rather than walk­ing ran­dom rows. 

Limitations

“While power­ful, drone tech­no­logy is not a sil­ver bul­let for every crop or con­di­tion,” admits Hey­denrych.

There are instances where drones will have problems performing to their maximum potential and these include 

•  Dense Can­op­ies: In crops with extremely dense can­op­ies, such as mature sugar cane or maize, sensors may struggle to pen­et­rate to the lower leaves or bases of the plants. This can obscure early signs of dis­ease that close to near the soil.
• Growth Stage Vari­ab­il­ity: Inter­pret­ing data requires con­text. For example, a young tree nat­ur­ally has a smal­ler can­opy and dif­fer­ent reflect­ance than a mature one. Without soft­ware that sep­ar­ates trees by age or size (using tree census data), a young healthy tree might be falsely flagged as ‘stressed’ by an index like NDRE.
• Int­ercr­opping: Fields with com­plex mixed plant­ing sys­tems present chal­lenges, as it becomes dif­fi­cult to isol­ate the spec­tral sig­na­ture of a spe­cific tar­get crop from the sur­round­ing veget­a­tion.

Getting the best results

Farm­ers do not need raw data; they need action­able intel­li­gence. For­tu­nately, the industry has moved bey­ond com­plex spread­sheets to intu­it­ive visual products, which can be obtained using processes that include the below.

• Ort­ho­mosaic and Zonal Maps: The most com­mon out­put is an ortho­mo­saic, a highly detailed map of the entire field, cre­ated by ‘stitch­ing together’ mul­tiple drone images. From this, soft­ware gen­er­ates zonal maps, which are typ­ic­ally col­our-coded for sim­pli­city: green for healthy, high-vigor areas, and red or yel­low for stressed, low-vigor zones.
• Per-tree Met­rics: for orch­ard crops, the data is highly gran­u­lar. Plat­forms can now seg­ment each tree, provid­ing indi­vidual met­rics for can­opy area (m²), volume (m³), and health. This allows farm­ers to identify spe­cific under­per­form­ing trees rather than gen­eral areas.
• P­rescr­iption Maps: The data can be con­ver­ted into pre­scrip­tion maps for vari­able rate applic­a­tion (VRA). These maps can be uploaded into tract­ors or spray drones to auto­mat­ic­ally apply fer­til­iser and other chem­ic­als only where needed, thereby redu­cing waste.
• Mobile Integ­ra­tion: Mod­ern plat­forms sync this data to mobile apps. This allows a farmer to stand in the field, see their loc­a­tion on the map, and walk dir­ectly to a ‘red’ tree flagged by the drone to inspect the prob­lem.

The cost of precision

On the ques­tion of cost, Hey­denrych offers estim­ates but emphas­ises that all fig­ures are approx­im­ate and can vary widely depend­ing on the applic­a­tion, required tech­no­logy, loc­a­tion, and ser­vice pro­vider.

“These fig­ures should not be con­sidered accur­ate fore­casts or a basis for liab­il­ity due to future mar­ket changes,” she warns.

“The invest­ment required for drone-­based scout­ing var­ies widely depend­ing on whether a farm chooses to own the equip­ment or hire a ser­vice,” she states. Her estim­a­tions are as fol­lows:

Hard­ware costs

• Entry-level: drones with basic RGB cam­eras suit­able for visual scout­ing can cost between $350 and $2,300.
• P­rof­essional multi-rotor: Robust units equipped with multis­pec­tral sensors range from $5,600 to $23,000.
• Fixed-wing sys­tems: High-end fixed-­wing drones for large-scale map­ping can range from $11,200 to $56,000.

Soft­ware and ser­vice costs

• So­ftware sub­scrip­tions: pro­cessing and ana­lyt­ics plat­forms typ­ic­ally charge annual fees ran­ging from a few hun­dred to sev­eral thou­sand rand, depend­ing on hec­tar­age and fea­ture depth.
• S­ervice Pro­viders: many farm­ers prefer to out­source ser­vices to pilots who man­age the flight and data pro­cessing. Rates typ­ic­ally range from R200 to R600/ha/flight.

On the ques­tion of return on Invest­ment (ROI), Hey­denrych says that while the upfront cost is a con­sid­er­a­tion, the ROI is driven by three factors:

• Reduced yield loss: detect­ing a pest out­break or irrig­a­tion fail­ure early can save a sig­ni­fic­ant per­cent­age of the har­vest.
• Optim­ised inputs: VRA allows farm­ers to treat only the affected areas, sig­ni­fic­antly lower­ing fer­til­iser and other chem­ical costs.
• Labour effi­ciency: AI-guided sampling reduces the need for manual scout­ing, allow­ing staff to focus only on con­firmed prob­lem areas.

Conclusion

“Drones are no longer a futur­istic nov­elty; they are a present-day real­ity trans­form­ing agri­cul­tural man­age­ment. By com­bin­ing high-resol­u­tion RGB, multis­pec­tral, and thermal sensors with AI-driven ana­lyt­ics, drones provide a dia­gnostic frame­work that is faster and more accur­ate than tra­di­tional meth­ods.

“They do not replace the farmer’s expert­ise but rather dir­ect it, turn­ing a walk in the field from a ran­dom search into a tar­geted mis­sion.

“As tech­no­logy advances, offer­ing deeper insights at lower costs, the view from above is becom­ing an essen­tial com­pon­ent of the farm down below,” Hey­denrych con­cludes.