Advances in Sustainable Crop Management: Integrating Precision Agriculture and Proximal Sensing
Abstract
:1. Introduction
2. Role of Precision Agriculture in Sustainable Crop Management
2.1. Map-Based Approach
2.1.1. Satellite Remote Sensing
Crop | Aim | Platform (Spatial Resolution or Distance from the Target) Satellite | Sensor | Reference |
---|---|---|---|---|
Cotton (Gossypium spp.) | Nitrogen VRT fertilization |
| RapidEye MSI: Blue (475 nm), green (555 nm), red (658 nm), red-edge (710 nm) and near-infrared (805 nm) | [36] |
Soil | Variable Rate Irrigation based on soil properties |
| Sentinel-2: B4 (Red): 665 ± 30 nm–B8 (Near-Infrared–NIR): 842 ± 115 nm | [40] |
Winter wheat | Compare RS and PS for site-specific crop management |
| Sentinel-2: B02 (Blue): 490 nm–B03 (Green): 560 nm–B04 (Red): 665 nm–B05 (Red Edge 1): 705 nm–B06 (Red Edge 2): 740 nm–B07 (Red Edge 3): 783 nm–B08 (NIR): 842 nm–B11 (SWIR): 1610 nm); Fritzmeier ISARIA: (660–780 nm) | [42] |
Potato and maize | Map-based site-specific seeding of seed potato production |
| Sentinel-2: (B04 (Red): 665 nm, B08 (NIR): 842 nm). | [43,53,54,55,60] |
Wheat and barley | Develop a model for estimating crop yield |
| Deimos-1: (Red): 630–690 nm–(NIR): 770–900 nm; Landsat 7: (B03 (Red)): 0.63–0.69 µm–(B04 (NIR)): 0.77–0.90 µm; Landsat 8: (B04 (Red)): 0.64–0.67 µm–B05 (NIR)): 0.85–0.88 µm; Sentinel-2 (S2A): (B04 (Red)): 665 nm–B08 (NIR)): 842 nm); Sentinel-2 (S2B): B04 (Red)): 665 nm-B08 (NIR)): 842 nm; | [48] |
Corn and soybean | Compare satellite sensors to assess field yield variability |
| WorldView-3 (WV-3): Coastal Blue: 0.426 µm- Blue: 0.479 µm- Green: 0.552 µm- Yellow: 0.610 µm- Red: 0.662 µm- Red-Edge: 0.726 µm- NIR1: 0.831 µm- NIR2: 0.910 µm; Planet (Dove-Classic Sensors): Blue: 0.485 µm - Green: 0.545 µm- Red: 0.630 µm- NIR: 0.820 µm; Harmonized Landsat Sentinel-2 (HLS): Green: ~0.560 µm- Red: ~0.660 µm- NIR (8A band of Sentinel-2): ~0.865 µm- SWIR: ~1.5 µm and ~2.2 µm. | [44] |
(Triticum aestivum L., cv. PRR58) | Compare different nitrogen VRT fertilization |
| Sentinel-2 (S2A): (B04 (Red)): 665 nm–B08 (NIR)): 842 nm). | [50] |
Wheat | Disease detection (powdery mildew) |
| SPOT-6 B1: Blue (455–525 nm)- B2 Green (530–590 nm)- B3: Red (625–695 nm)- B4: Near Infrared (NIR) (760–890 nm). | [51] |
Wheat | Growth monitoring (Biomass, moisture and structure) |
| Polarimetric SAR Interferometry (Pol-InSAR): L-, C- and X-Bands | [56] |
Wheat | Disease detection pathogens powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita). |
| QuickBird Satellite: Blue: 450–520 nm–Green: 520–600 nm–Red: 630–690 nm–Near-Infrared (NIR): 760–900 nm; HyMap Airborne: 126 bands (450 nm-2480 nm); ASD FieldSpec Pro (350–2500 nm). | [57] |
Pigeonpea (Cajanus cajan) plants | Disease detection (Fusarium wilt) |
| Red-Edge (690–740 nm), Short Wave Infra-Red (SWIR) (1510–1680 nm), and Green (530–570 nm) | [59] |
Onion | Comparing data acquired by fixed-wing UAV satellite to crop monitoring. |
| UAV: Parrot Sequoia MS (Green (530–570 nm), Red (640–680 nm), Red Edge (730–740 nm), and NIR (770–810 nm); Sentinel-2: Blue 426–558 (width 66)–Green 523–595 (width 36-) Red 633–695 (width 31)–NIR 726–938 (width 106); PlanrtScope: Blue 464–517 (width 26.5)–Green 547–585 (width 19)–Red 650–682 (width 16)–NIR 846–888 (width 21) | [58] |
2.1.2. Unmanned Aerial Vehicles
2.2. Sensor-Based Approach
Objective | Experimental Condition | Crop (s) | Stress Evaluated | Sensors | Reference | |
---|---|---|---|---|---|---|
Type | Specifications | |||||
Develop a modular robot system for automatic disease detection. | Controlled greenhouse environment | Vitis vinifera, (cv. Cabernet Sauvignon) | Powdery mildew (Erysiphe necator) | CIR | 3-CCD, R-G-NIR camera (MS4100, DuncanTech, Auburn, CA, USA): Green (540 nm), Red (660 nm), and NIR (800 nm); | [12] |
Develope. A human-robot framework for target detection, involving a remote human operator and a robotic platform equipped with target detection algorithms. | Field | Grapevines | N.S. | RGB | (IDS Inc. (Washington, DC, USA), uEye USB video camera with a Wide VGA [752 × 480] resolution); | [104] |
Develop an image processing based on a variable-rate chemical sprayer assisted with remote monitoring | Field | Coconut plantations | Two-colored coconut leaf beetles (Brontispa longissima) Coconut black-headed caterpillars (Opisina arenosella) Coconut rhinoceros beetles (Oryctes rhinoceros) | RGB | (IDS Inc., uEye USB video camera with a Wide VGA [752 × 480] resolution); | [105] |
Develop a variable-rate spraying system for precise application of agrochemicals based on plant disease severity. | Field | Paddy (rice) | White-tip disease caused by Aphelenchoides besseyi Christie. | RGB | Web cameras (Logitech Pro 9000, San Jose, CA, USA); | [106] |
Evaluate a system based on digital image processing for detection of weeds in row crops | Field | Maize (Zea maize L.), Sugar beets (Beta vulgaris L.), and Sunflower (Helianthus annuus L.). | Weed | CIR | RGB imager with an R/NIR filter,(Robert Bosch GmbH).; | [107] |
Evaluate the possibility of using a low-cost imaging system to drive a precision orchard spraying system. | Both laboratory and field test | Olive tree orchard | N.S | RGB | Camera (TSCO, VGA (640 × 480), 30 fps, 10 Megapixels). | [108] |
Develop a low-cost and smart technology for precision weed management. | Field | N.S | Weeds: | RGB | Low-cost web cameras (LOGITECH c920, Newark, CA, USA) 640 × 480 pixels resolution. | [13] |
Develop a smart technology for precision weed management. | Field | Maize, Winter wheat, Winter barley, and Sugar beets | Weeds: Winter Rape:Alopecurus myosuroides, Apera spica-venti, broad-leaves; Maize: Echinochloa crus-galli L., Chenopodium album L., Galinsoga parviflora Cav., Solanum nigrum L.; Winter wheat/barley: Echinochloa crus-galli L., Chenopodium album L., Galinsoga parviflora Cav., Solanum nigrum L.; Suger beets: Chenopodium album, Galium aparine, Alopecurus myosuroides | RGB | Digital bi-spectral cameras (N.S). | [109] |
Develop a site-specific agrochemical application. | Both controlled laboratory conditions and field. | Potato (Solanum tuberosum L.) | Weed (lambsquarters); Simulated disease (early blight) infected plants at a laboratory scale. | RGB | Two types of cameras (Canon PowerShot SX540 HS camera and Logitech C270 HD Webcam). | [14] |
Evaluate the performance accuracy of a modified variable rate granular (MVRG) fertilizer spreader on a tractor. | Field | Wild blueberry (Vaccinium angustifolium Ait.) | Fertilizer application | RGB | Six µEye color cameras (UI-1220SE/C, IDS Imaging Development System Inc., Woburn, MA, USA) | [23] |
Design an automated prototype VR sprayer on the tractor. | Field | Wild blueberries (Vaccinium angustifolium). | Fungicide and fertilizer application. | RGB | Four µEye digital color cameras (UI-1220SE/C, IDS Imaging Development System Inc., Woburn, MA, USA). | [22,102] |
Testing the intelligent orchard pesticide precision sprayer. | Field | Peach and Apricot trees, and grapevines | Pesticide spraying based on leaf Wall Area (LWA) | RGB_Depht | Microsoft’s Kinect | [110] |
Development of Deep Learning-Based Variable Rate Agrochemical Spraying System for Targeted Weeds | Both laboratory and field | Strawberry | Weed: spotted spurge and Shepherd’s purse. | RGB | Two digital cameras with resolutions ranging from 3000 × 2000 to 1500 × 1000 pixels | [111] |
Develop a multi-parametric system for variable rate nitrogen application. | Field | Winter wheat (Triticum aestivum L.) | Nitrogen fertilizers application | MSI N sensor | The Yara N-Sensor ALS 2 (Yara GmbH and Co. KG, based in Dülmen, Germany): (670, 730, 740, and 770 nm). | [103] |
Design and evaluate an on-the-go VR fertilization system for the application of phosphate (P2O5). | Field | Maize | Phosphorus (P) fertilizer | VIS-NIR | A portable, fiber-type, VIS-NIR spectrophotometer (Zeiss Corona 45 visnir 1.7, Germany): (305–1711 nm): (401–1135 nm ± 3.2 nm) and (1135–1663 nm ± 6 nm). | [97] |
Develop a low-cost agricultural robot for spraying fertilizers. | Greenhouse | Rosemary crops | Spraying liquid fertilizers and pesticides. | RGB | The GoPro Hero 5 action video camera. | [112] |
Design an intelligent robot equipped with a wireless control to monitor the nutritional needs of the spinach plant. | Greenhouse | Baby spinach (Spinacia oleracea) | Iron deficiency | RGB | ESP32CAM digital camera with a resolution of 1200 × 1622 pixels. | [25] |
Develop an autonomous robot-driven CSSF and evaluate its agro-economic and environmental feasibility in maize production. | Field | Maize | Site-specific seeding and nitrogen (N) fertilization solution | VIS-NIR | on-line vis-NIRS (Visible and Near-Infrared Spectroscopy) system developed by Mouazen (2006). | [113] |
Develop an online plant health monitoring system to assess overall plant health in real-time. | Field | Maize, Wheat, Soybeans, and Tomatoes. | Pathogens or nutrient deficiencies relevant to the chosen crop would be considered, such as fungal diseases, pest infestations, or nitrogen deficiency. | RGB-NIR | RGB and NIR imaging cameras (N.S). | [114] |
Develop a flexible robotic-based approach with proximal sensing tools (XF-ROVIM) specifically developed to detect X. Fastidiosa on olive orchard | Field | Olive tree ochard | Xylella fastidiosa (X. fastidiosa) infection | CIR; MSI; HyS Imager; Thermal camera | CIR: two digital single-lens reflex (DSLR) modified cameras (EOS 600D, Canon Inc., Tokyo, Japan); MSI: (CMS-V, Silios Technologies, Peynier, France) that can obtain simultaneous images at eight different wavelengths (558, 589, 623, 656, 699, 732, 769, and 801 nm); HyS: (spectrograph Imspector V10, Specim Spectral Imaging Ltd., Oulu, Finland (400 nm–1000 nm) + camera uEye 5220CP, iDS Imaging Development Systems (GmbH, Obersulm, Germany); Thermal camera: (A320, FLIR Systems, Wilsonville, OR, USA). | [115] |
Develop a remote-controlled field robot (RobHortic) for inspecting the presence of pests and diseases in horticultural crops using proximal sensing. | Both laboratory and filed | Carrots | Disease (Candidatus Liberibacter solanacearum). | CIR; MSI; HyS Imager; Thermal camera | MSI: (CMS-V, Silios Technologies, France) (558, 589, 623, 656, 699, 732, 769, and 801 nm,); CIR: three DSLR (Digital Single Lens Reflex) cameras (EOS 600D, Canon Inc, Japan), two modified to capture images in near-infrared (NIR) from 400 to 1000 nm); HyS:(InSpectral-VNIR, Infaimon SL, Spain) (410–1130 nm); Thermal camera: (A320, FLIR systems, Wilsonville, OR, USA). | [116] |
Develop an autonomous machine vision-based system for precise nitrogen fertilizing management to improve nitrogen use efficiency in greenhouse crops. | Greenhouse | Cucumber. | Site-specific fertilizer | RGB | CCD color camera (mod. DF-7107, Sony, Tokyo, Japan) | [117] |
Design, development, and testing of a robot for plant-species–specific weed management | Filed | Cotton, Wild oats, and Sowthistle | Weed | RGB | IDS UI-1240SE 1.3 MP global shutter camera. | [118] |
Develop a prototype of a robotic platform to address the specific needs of this field type at an individual plant level rather than per strip or field section. | Filed | Cabbage and red cabbage | Site-specific fertilizer | MSI and RGB. | MSI: Parrot Sequoia multi-spectral (MS) camera; RGB: Vorsch RGB; | [119] |
Develop a robotic disease detection system in greenhouses | Greenhouse | Bell peppers | Powdery mildew (PM) and Tomato spotted wilt virus (TSWV) | RGB | RGB camera (PowerShot SX210 IS, Canon, USA) 4320 × 3240 pixels resolution. | [120] |
Develop a robotic disease detection system in greenhouses | Greenhouse | Bell peppers | Powdery mildew (PM) and Tomato spotted wilt virus (TSWV) | RGB; MSI | RGB RGB camera (LifeCam NX-6000 WebCam, Microsoft, Redmond, WA, USA) with a resolution of 1600 × 1200 pixels; MSI: NIR-R-G multispectral camera (ADC Lite, 520–920 nm, equivalent to TM2, TM3, and TM4, Tetracam, Chatsworth, CA, USA) with a resolution of 2048 × 1536 pixels, and a single-laser-beam distance sensor (DT35, SICK, Waldkirch, Germany). | [121] |
Develop a robotic platform for single-plant fertilization | Field | Organic vegetable | Single Plant Fertilization | MSI | The multispectral camera (model Sequoia; Parrot Drones SAS, France). | [122] |
Develop a smart irrigation system | Field | Soil | Smart irrigation | RGB | Digital camera (Model Nikon D5300) (6000 × 4000 pixels resolution). | [101] |
Develop a variable rate fertilizer applicator to detect real-time deficiency of N | Field | Wheat | Site-specific fertilizer | VIS-NIR | Greenseeker handheld sensor (Trimble Inc., Sunnyvale, CA, USA) | [123] |
Develop a computer-vision system for detecting crop plants at different growth stages for robotic weed management | Field | Lettuce (Lactuca, L.) and broccoli (Brassica oleracea L. var. botrytis L.). | Weeds: bromegrass (Bromus inermis Leyss), pigweed (Amaranthus spp.), lambsquarters (Chenopodium album), waterhemp (Amaranthus rudis), barnyardgrass (Echinochloa crus-galli), bindweed (Convolvulus arvensis), purslane (Portulaca oleracea), and white clover (Trifolium repens) | RGb-Depth | RGB-D sensor (Kinect version 2; Microsoft) | [124] |
3. Proximal Sensing Techniques for Crop Health Monitoring
3.1. Characterizing the Physiological Response to Stress: The Principles of Optoelectronic Techniques
3.2. Abiotic Crop Stresses
3.3. Biotic Crop Stresses
4. Results and Discussions
5. Conclusions and Future Trends
- The effect of using these technologies, such as UAVs, autonomous robots, and multispectral sensors, offering precise and high-resolution data, allows the farmers to estimate, with a decision based on objective data, the quantity, time, and location of agrochemicals application.
- A gap exists between sensors used for monitoring and those for real-time decision-making. Multi-band sensors are frequently used in monitoring. However, digital sensors like RGB cameras are preferred for real-time applications.
- Despite the multispectral and RGB-modified (CIR) sensors being optimal for close-range monitoring, challenges include image alignment and noise removal and/or reduction, which require better algorithms and image processing techniques.
- Integration with artificial intelligence allows for the development of predictive models and real-time decision-making systems that improve crop management and resource optimization.
- Further research is needed to improve image processing algorithms, enhance sensor calibration techniques, and develop more efficient data management systems.
Author Contributions
Funding
Conflicts of Interest
References
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Crop | Aim | Platform (Spatial Resolution or Distance from the Target) | Sensors | References | |
---|---|---|---|---|---|
Type | Specifications | ||||
Corn (Zea mays L.) | Disease detection (Setosphaeria turcica) | DJI Matrice 600 UAV (DJI, Shenzhen, China)) (6 m) | RGB | Sony Alpha 6000 camera | [87] |
Corn | Prediction and mapping of soil properties and corn yield | Aircraft: Digital Elevation Model (DEM) Collection (1 m); MSI data collection (0.3 m) | RGB + LiDAR | RGB (Leica ADS80 digital camera) + LiDAR | [38] |
Corn variety: DeKalb Brand-DKC67–72 | Monitoring Different Physiological Stages for Yield Prediction and Input Resource Optimization | N.S. (from 60 m to 30 m) | MSI | MicaSense RedEdge™: Blue (475 nm ± 32 nm), green (560 nm center, ± 27 nm), red (668 ± 16 nm), red-edge (717 nm ± 12 nm), and near-infrared (842 nm ± 57 nm). | [75] |
Wheat, barley, and oats | Yield Prediction | Airinov Solo 3DR (Parrot Drone SAS, Paris, France) (150 m) | 1 × 1 m/px | MSI: | SEQUOIA (Parrot Drone SAS, Paris, France): Green: 550 nm ± 40 nm Red: 660 nm ± 40 nm Red Edge: 735 ± 10 nm Near-Infrared (NIR): 790 nm ±: 20 nm. | [66] |
Soybean | Weed detection | DJI Matrice 600 pro ((DJI, Shenzhen, China)), (20 m) | MSI | SEQUOIA (Parrot Drone SAS, Paris, France): Green: 550 nm ± 40 nm Red: 660 nm ± 40 nm Red Edge: 735 ± 10 nm Near-Infrared (NIR): 790 nm ±: 20 nm. | [88] |
Cotton and sunflower | Weed detection | Quadcopter model MD4–1000 (microdrones GmhH, Siegen, Germany) (30–60 m) | CIR | Sony ILCE-6000 camera, + NIR. | [65] |
Sunflower. | Weed detection | Quadrocopter md4–1000 (microdrones GmbH, Siegen, Germany) | RGB + MSI | MSI: TetraCam mini-MCA-6 (TetraCam Inc., Chatsworth, CA, USA) (blue (B, 450 nm), green (G, 530 nm), red (R, 670 and 700 nm), Redge (740 nm) and near-infrared (NIR, 780 nm) RGB: Olympus PEN E-PM1 (Olympus Corporation, Tokyo, Japan) | [64] |
Pea and strawberry | Weed detection | DJI Spark (Multirotor) (2 m) | 0.3 cm/px | RGB | CMOS sensor (3968 × 2976 pixels) | [89] |
Lettuce | Weed detection | Multi-rotor DJI Mavic Pro (2 m) | 0.22 cm/px | MSI | SEQUOIA (Parrot Drone SAS, Paris, France): Green: 550 nm ± 40 nm Red: 660 nm ± 40 nm Red Edge: 735 ± 10 nm Near-Infrared (NIR): 790 nm ±: 20 nm. | [61] |
Olive tree | Pest detection (Xylella fastidiosa subsp. pauca (Xfp)) | Multi-rotor DJI Mavic Pro (70 m) | 6.6 cm/px | MSI | SEQUOIA (Parrot Drone SAS, Paris, France): Green: 550 nm ± 40 nm Red: 660 nm ± 40 nm Red Edge: 735 ± 10 nm Near-Infrared (NIR): 790 nm ± 20 nm. | [76] |
Wheat (cv). ‘Mingxian 169’ | Diseases detection (Yellow rust) | Six-rotor electric UAV system (DJI Innovations, Shenzhen, China) (30 m) | 1.2 cm/px | HyS | UHD 185 (Cubert GmbH, Ulm, Baden-Württemberg, Germany): 450–950 nm ±: 4 nm. | [77] |
Tomato | Diseases detection (Tomato Yellow Leaf Cur)–TYLC; Target Spot ((Corynespora cassiicola)–TS; Bacterial Spot (Xanthomonas perforans)–BS) | (Matrice 600 Pro Hexacopter, DJI, Shenzhen, China) (30 m) | 1.03 cm/px | HyS | Pika L 2.4 hyperspectral camera (Resonon, Bozeman, MT, USA): 380 to 1020 nm. | [78] |
Potato | Disease detection (late blight) | UAV (N.S.) (80 m) | 4–5 m/px | HyS | Rikola Ltd., (Oulu, Finland): 600–800 nm | [79] |
Maize | Weed detection/spraying |
| CIR/MSI | CIR: Modified Canon S110 camera (Red (660 nm), green (520 nm), blue (450 nm), and near-infrared (NIR; 850 nm)). 2015: MSI: Agrosensor multispectral camera by AIRINOV (Channels: Green (550 nm), red (660 nm), red edge (735 nm), NIR (790 nm)). | [70] |
Vineyard | Spraying | Hexacopter (model: DroneHEXA, Dronetools SL, Sevilla, Spain) (95 m) | MSI | MicaSense RedEdge: Red:668 nm ±5 nm, Green: 560 nm ± 10 nm, Blue: 475 nm ± 10 nm, RedEdge: 717 nm ± 5 nm, Near Infrared (NIR): 840 nm ± 20 nm; | [11,90] |
Vineyard | Diseases detection downy mildew (Plasmopara viticola/spraying | Hexacopter (model: CondorBeta, Dronetools SL, Sevilla, Spain) (95 m) | MSI | MicaSense RedEdge: Red:668 nm ±5 nm, Green: 560 nm ± 10 nm, Blue: 475 nm ± 10 nm, RedEdge: 717 nm ± 5 nm, Near Infrared (NIR): 840 nm ± 20 nm; | [67] |
Barley (H. vulgare L). | Phenotyping response of barley to different nitrogen fertilization treatments | Mikrokopter Oktokopter 6S12 XL eight rotor UAV (HiSystems GmbH, Moomerland, Germany) (50 m) | RGB (10 mm/px); THERMAL CAMERA (54 mm/px) | RGB, Thermal and MSI cameras | RGB: Panasonic GX7 digital camera (Panasonic Corporation, Osaka, Japan); MSI:Tetracam (Tetracam, Inc., Gainesville, FL, USA) mini MCA (Multiple Camera Array): 450 ± 40 nm, 550 ± 10 nm, 570 ± 10 nm, 670 ± 10 nm, 700 ± 10 nm, 720 ± 10 nm, 780 ± 10 nm, 840 ± 10 nm, 860 ± 10 nm, 900 ± 20 nm, 950 ± 40 nm; TH: FLIR Tau2 640 (FLIR Systems, Nashua, NH, USA). | [73] |
Wheat | Water stress | DJI Matrice 100 quadcopter (DJI, Shenzhen, China) (35 m) | 2.43/px | MSI | MicaSense RedEdge: Blue: 475 nm, Green: 560 nm, Red: 668 nm, Red Edge: 717 nm, Near-Infrared: 840 nm. | [84] |
Vineyard | Disease detection (Mildew disease) | Quadcopter drone (25 m) | 1 cm2/px | CIR | two camera sensors MAPIR Survey2(RGB + NIR) | [80] |
Winter oilseed rape (Brassica napus L.) | Nitrogen stress | Matrice 600 UAV (DJI, Shenzhen, China) (20 m) | 1.86 cm/px | RGB | Nikon D800 (Nikon, Inc., Tokyo, Japan) | [83] |
Vineyard cv. –Vermentino’, ‘Cagnulari’, and ‘Cabernet Sauvignon’ grapevines | Discriminate several water stress condition |
| Thermal camera and Thermal imaging camera | Thermal camera: (FLIR TAU II 320, FLIR Systems, Inc., Wilsonville, OR, USA); Thermal imaging camera: (InfRec R500Pro, Nippon Avionics Co. Ltd., Tokyo, Japan) with a resolution of 640 × 480 pixels, operating in the 8–14 µm waveband range, and equipped with a red, green, and blue (RGB) | [85] |
Rice | Nitrogen accumulation estimation | Mikrokopter OktoXL: RGB (50 m) | 13 mm/px; CIR: (100 m) | 36 mm/px; MSI: (100 m) | 56 mm/px. | RGB, CIR, and MSI | RGB: Canon 5D Mark III (Canon Inc., Tokyo, Japan); CIR: Canon PowerShot SX260 + NIR; MSI: Tetracam mini-MCA6 (Tetracam Inc., Chatsworth, CA, USA: B (490 nm ± 10 nm), Green (550 nm ± 10 nm), Red (680 nm ± 10 nm), Red Edge (720 nm ± 10 nm), NIR1 (800 nm ± 10 nm) and NIR2 (900 nm ± 10 nm). | [82] |
Maize | Crop grow status evaluation based on canopy chlorophyll content | DJI M600 Pro (DJI, Shenzhen, China) UAV 30 m) | MSI | Red Edge–MX: Blue: 475 nm ± 32 nm); Green: 560 nm ± 27 nm) Red: 668 nm ± 14 nm) Red Edge (RE): 717 nm ± 12 nm) Near-Infrared (NIR): 840 nm ±57 nm). | [81] |
Wheat (cv. Yangmai 23, Zhenmai 12, and Ningmai 13); Rice (cv. Nanjing 9108, Yongyou 2640, and Wuyunjing 32) | Investigated the effects of different nitrogen (N) fertilizer rates, | eBee fixed-wing UAV (SenseFly, Cheseaux-sur-Lausanne, Switzerland) (70 m) | MSI | SEQUOIA (Parrot Drone SAS, Paris, France): Green: 550 nm ± 40 nm Red: 660 nm ± 40 nm Red Edge: 735 ± 10 nm Near-Infrared (NIR): 790 nm ±: 20 nm. | [72] |
Wheat (Triticum aestivum) | Investigated the effects of different nitrogen (N) fertilizer rates | eBee fixed-wing UAV (SenseFly, Cheseaux-sur-Lausanne, Switzerland) | MSI | SEQUOIA (Parrot Drone SAS, Paris, France): Green: 550 nm ± 40 nm Red: 660 nm ± 40 nm Red Edge: 735 ± 10 nm Near-Infrared (NIR): 790 nm ±: 20 nm. | [71] |
Winter wheat | Water stres | DJI M300 Pro UAV (Shenzhen DJI Sciences and Technologies Ltd., Shenzhen, China): MSI: (50 m) | 3.5 cm/px; Thermal Camera: (50 m) | 4.5 cm/px | MSI and Thermal camera | MSI: RedEdge-MX (MicaSense AgEagle, Wichita, KS, USA): Blue (465–485 nm), Green (550–570 nm), Red (663–673 nm), Red Edge (712–722 nm), and Near-Infrared (820–860 nm); Therma camera: Zenmuse H20T. | [86] |
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Laveglia, S.; Altieri, G.; Genovese, F.; Matera, A.; Di Renzo, G.C. Advances in Sustainable Crop Management: Integrating Precision Agriculture and Proximal Sensing. AgriEngineering 2024, 6, 3084-3120. https://doi.org/10.3390/agriengineering6030177
Laveglia S, Altieri G, Genovese F, Matera A, Di Renzo GC. Advances in Sustainable Crop Management: Integrating Precision Agriculture and Proximal Sensing. AgriEngineering. 2024; 6(3):3084-3120. https://doi.org/10.3390/agriengineering6030177
Chicago/Turabian StyleLaveglia, Sabina, Giuseppe Altieri, Francesco Genovese, Attilio Matera, and Giovanni Carlo Di Renzo. 2024. "Advances in Sustainable Crop Management: Integrating Precision Agriculture and Proximal Sensing" AgriEngineering 6, no. 3: 3084-3120. https://doi.org/10.3390/agriengineering6030177