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A peer-reviewed article of this preprint also exists.
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Submitted:
05 February 2024
Posted:
06 February 2024
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Type | Title | Extraction method or description | Remarks |
---|---|---|---|
vegetation index | Normalized Vegetation Index (NDVI) | (NIR - R) / (NIR + R) | Reflect the coverage and health status of plants |
Red edge chlorophyll vegetation index (ReCl) | (NIR / RED) - 1 | Display the photosynthetic activity of the canopy | |
Enhanced Vegetation Index (EVI2) | 2.5*(NIR - R) / (NIR + 2.4* R +1)*(1 - ATB) | Accurately reflect the growth of vegetation | |
Ratio Vegetation Index (RVI) | NIR/R | Sensitive indicator parameters of green plants, which can be used to estimate biomass | |
Difference Vegetation Index (DVI) | NIR-R | Sensitive to soil background, beneficial for monitoring vegetation ecological environment | |
Vertical Vegetation Index (PVI) | ((SR-VR)2+(SNIR-VNIR)2)1/2 | S represents soil emissivity, V represents vegetation reflectance | |
Transformed Vegetation Index (TVI) | (NDVI+0.5)1/2 | Conversion of chlorophyll absorption | |
Green Normalized Difference Vegetation Index (GNDVI) | (NIR-G)/(NIR+G) | Strong correlation with nitrogen | |
Normalized Difference Red Edge Index (NDRE) | (NIR-RE) / (NIR +RE) | RE represents the emissivity of the red-edge band | |
Red Green Blue Vegetation Index (RGBVI) | (G-R)/(G+R) | Measuring vegetation and surface red characteristics | |
Green Leaf Vegetation Index (GLI) | (2G-B-R)/(2G+B+R) | Measuring the degree of surface vegetation coverage | |
Excess Green(ExG) | (2G-R-B) | Small-scale plant detection | |
Super Green Reduced Red Vegetation Index (ExGR) | 2G-2.4R | Small-scale plant detection | |
Excess Red(ExR) | 1.4R-G | Soil background extraction | |
Visible Light Atmospheric Impedance Vegetation Index (VARI) | (G –R)/(G+R-B) | Reduce the impact of lighting differences and atmospheric effects | |
Leaf Area Vegetation Index (LAI) | leaf area (m2) / ground area (m2) | The ratio of leaf area to the soil surface covered | |
Atmospheric Resilience Vegetation Index (ARVI) | (NIR-(2*R)+B)/(NIR+(2*R)+B) | Used in areas with high atmospheric aerosol content | |
Modified Soil Adjusted Vegetation Index (MSAVI) | (2*NIR+1-sqrt((2*NIR+1)2-8*(NIR-RED)))/2 | Reduce the impact of soil on crop monitoring results | |
Soil Adjusted Vegetation Index (SAVI) | (NIR-R)*(1+L)/(NIR+R+L) | L is a parameter that varies with vegetation density | |
Optimize Soil Adjusted Vegetation Index (OSAVI) | (NIR–R) / (NIR + R +0.16) | Using reflectance from NIR and red spectra | |
Normalized Difference Water Index (NDWI) | (BG - BNIR ) / (BG +BNIR) | Research on vegetation moisture or soil moisture | |
Conditional Vegetation Index (VCI) | The ratio of the current NDVI to the maximum and minimum NDVI values during the same period time over the years | Reflect the growth status of vegetation within the same physiological period | |
Biophysical parameters | Leaf Area Index (LAI) | Total leaf area/land area | The total leaf area of plants per unit land area is closely related to crop transpiration, soil water balance, and canopy photosynthesis |
Photosynthetically active radiation component (FPAR) | Proportion of absorbable photosynthetically active radiation in photosynthetically active radiation (PAR) | Important biophysical parameters commonly used to estimate vegetation biomass | |
Growth environment parameters | Conditional Temperature Index (TCI) | The ratio of the current surface temperature to the maximum and minimum surface temperature values over the same period time over the years | Reflecting surface temperature conditions, widely used in drought inversion and monitoring |
Conditional Vegetation Temperature Index (VTCI) | The ratio of LST differences between all pixels with NDVI values equal to a specific value in a certain research area | Quantitatively characterizing crop water stress information | |
Temperature Vegetation Drought Index (TVDI) | Inversion of surface soil moisture in vegetation-covered areas | Analyzing spatial changes in drought severity | |
Vertical Drought Index (PDI) | The normal of the soil baseline perpendicular to the coordinate origin in the two-dimensional scatter space of near-infrared and red reflectance | The spatial distribution characteristics commonly used for soil moisture |
Crop varieties | Author | Year | Task | Network framework and algorithms | Result |
---|---|---|---|---|---|
corn | Wei Yang[41] | 2021 | Predict the yield of corn | CNN | AP: 75.5% |
Monica F. Danilevicz[42] | 2021 | Predict the yield of corn | tab-DNN and sp-DNN | R2: 0.73 | |
Chandan Kumar[43] | 2023 | Predict the yield of corn | LR, KNN, RF, SVR, DNN | R2: 0.84 | |
Danyang Yu[44] | 2022 | Estimate biomass of corn | DCNN, MLR,RF, SVM | R2: 0.94 | |
Ana Paula Marques Ramos[45] | 2020 | Predict the yield of corn | RF | R2: 0.78 | |
rice | Md. Suruj Mia[46] | 2023 | Predict the yield of rice | CNN | RMSPE: 14% |
Emily S. Bellis[47] | 2022 | Predict the yield of rice | 3D-CNN, 2D-CNN | RMSE: 8.8% | |
wheat | Chaofa Bian[48] | 2022 | Predict the yield of wheat | GPR | R2: 0.88 |
Shuaipeng Fei[36] | 2023 | Predict the yield of wheat | Ensemble learning algorithms of ML | R2: 0.692 | |
Zongpeng Li[56] | 2022 | Predict the yield of wheat | Ensemble learning algorithms of ML | R2: 0.78 | |
Yixiu Han[49] | 2022 | Estimate biomass AGB of Wheat | GOA-XGB | R2: 0.855 | |
Rui Li[57] | 2022 | Estimate yield of wheat | RF | R2: 0.86 | |
Xinbin Zhou[50] | 2021 | Calculate the yield and protein content of wheat | SVR, RF, and ANN | R2: 0.62 | |
Yuanyuan Fu[54] | 2021 | Estimate biomass of wheat | LSSVM | R2: 0.87 | |
Falv Wang[52] | 2022 | Estimate biomass of wheat | RF | R2: 0.97 | |
Malini Roy Choudhury[53] | 2021 | Calculate the yield of wheat | ANN | R2: 0.88 | |
Falv Wang[59] | 2023 | Predict the yield of wheat | MultimodalNet | R2: 0.7411 | |
Ryoya Tanabe[55] | 2023 | Predict the yield of wheat | CNN | RMSE: 0.94 t ha− 1 | |
Prakriti Sharma[51] | 2022 | Estimate biomass of oat | PLS, SVM, ANN, RF | r: 0.65 | |
Alireza Sharif[58] | 2020 | calculation yield of barley | GPR | R2: 0.84 | |
beans | Maitiniyazi Maimaitijiang[60] | 2020 | Predict the yield of soybean | DNN-F2 | R2: 0.72 |
Jing Zhou[61] | 2021 | Predict the yield of soybean | CNN | R2: 0.78 | |
Paulo Eduardo Teodoro[35] | 2021 | Predict the yield of soybean | DL and ML | r: 0.44 | |
Mohsen Yoosefzadeh-Najafabadi[62] | 2021 | Predict yield and biomass | DNN-SPEA2 | R2: 0.77 | |
Mohsen Yoosefzadeh-Najafabadi[63] | 2021 | Predict the yield of soybean seed | RF | AP: 93% | |
Yujie Shi[64] | 2022 | Predict AGB and LAI | SVM | R2: 0.811 | |
Yishan Ji[65] | 2022 | Estimate plant height and yield of broad beans | SVM | R2: 0.7238 | |
Yishan Ji[66] | 2023 | Predict biomass and yield of broad beans | Ensemble learning algorithms of ML | R2: 0.854 | |
potato | Yang Liu[67] | 2022 | Estimate biomass of potatoes | SVM, RF, GPR | R2: 0.76 |
Chen Sun[68] | 2020 | Predict the yield of potato tuber | ridge regression | R2: 0.63 | |
cotton | Weicheng Xu[69] | 2021 | Predict the yield of cotton | BP neural network | R2: 0.854 |
sugarcane | Chiranjibi Poudyal[70] | 2022 | Predict component yield of sugarcane | GBRT | AP: 94% |
Romário Porto de Oliveira[71] | 2022 | Predict characteristic parameters of sugarcane | RF | R2: 0.7 | |
spring tea | Zongtai He[72] | 2023 | Predict fresh yield of spring tea | PLMSVs | R2: 0.625 |
alfalfa | Luwei Feng[73] | 2020 | Predict yield | Ensemble learning algorithms of ML | R2: 0.854 |
meadow | Matthias Wengert[74] | 2022 | Predict the yield of meadow | CBR | R2: 0.87 |
ryegrass | Joanna Pranga[75] | 2021 | Predict the yield of ryegrass | PLSR, RF, SVM | RMSE: 13.1% |
red clover | Kai-Yun Li[76] | 2021 | Estimate the yield of red clover | ANN | R2: 0.90 |
tomato | Kenichi Tatsumi[77] | 2021 | Predict biomass and yield of tomato | RF, RI, SVM | rMSE: 8.8% |
grape | Rocío Ballesteros[78] | 2020 | Estimate the yield of the vineyard | ANN | RE: 21.8% |
apple | Riqiang Chen[79] | 2022 | Predict the yield of apple tree | SVR, KNN | R2: 0.813 |
almond | Minmeng Tang[80] | 2020 | Estimate yield of almond | Improved CNN | R2: 0.96 |
Crop varieties | Author | Year | Task | Network framework and algorithms | Result |
---|---|---|---|---|---|
wheat | Maria Bebie[81] | 2022 | Predict the yield of wheat | RF, KNN, BR | R2: 0.91 |
Elisa Kamir[82] | 2020 | Predict the yield of wheat | SVM | R2: 0.77 | |
Yuanyuan Liu[83] | 2022 | Predict the yield of wheat | SVR | R2: 0.87 | |
rice | Nguyen-Thanh Son[84] | 2022 | Predict the yield of rice | SVM, RF, ANN | MAPE:3.5% |
coffee tree | Carlos Alberto Matias de Abreu Júnior[85] | 2022 | Predict the yield of coffee tree | NN | R2: 0.82 |
rubber | Yang Liu[86] | 2023 | Predict the yield of rubber | LR | R2: 0.80 |
cotton | Patrick Filippi[87] | 2020 | Predict the yield of cotton | RF | LCCC:0.65 |
corn | Johann Desloires[88] | 2023 | Predict the yield of corn | Ensemble learning algorithms of ML | R2: 0.42 |
foodstuff | Fan Liu[89] | 2023 | Predict the yield of foodstuff | LSTM | R2:0.989 |
Crop varieties | Author | Year | Task | Network framework and algorithms | Result |
---|---|---|---|---|---|
kiwifruit | Jafar Massah[93] | 2021 | Count of fruit quantity | SVM | R2: 0.96 |
Youming Zhang[94] | 2022 | Calculate the leaf area index of kiwifruit | RFR | R2: 0.972 | |
corn | Youming Zhang[95] | 2020 | Predict the yield of corn | BP,SVM,RF,ELM | R2: 0.570 |
Meina Zhang[96] | 2020 | Estimate yield of corn | Regression Analysis | MAPE: 15.1% | |
apple | Amine Saddik[97] | 2023 | Count Apple fruit | Raspberry | AP: 97.22% |
Toona sinensis | Wenjian Liu[98] | 2021 | Predict aboveground biomass | MLR | R2: 0.83 |
cotton | Javier Rodriguez-Sanchez[99] | 2022 | Estimate the yield of Cotton | SVM | R2: 0.93 |
Crop varieties | Author | Year | Task | Network framework and algorithms | Result |
---|---|---|---|---|---|
corn | Canek Mota-Delfin[107] | 2022 | Detect and count corn plants | YOLOv4, YOLOv5 series | mAP: 73.1% |
Yunling Liu[108] | 2020 | Detect and count corn ears | Faster R-CNN | AP: 94.99% | |
Honglei Jia[109] | 2020 | Detect and count corn ears | VGG16 | mAP: 97.02% | |
wheat | Yixin Guo[110] | 2022 | Detect and count wheat ears | SlypNet | mAP: 99% |
Petteri Nevavuori[111] | 2020 | Predict wheat yield | 3D-CNN | R2: 0.962 | |
Ruicheng Qiu[112] | 2022 | Detect and count wheat ears | DCNN | R2: 0.84 | |
Yao Zhaosheng[113] | 2022 | detect wheat spikes Rapidly | YOLOX-m | AP: 88.03% | |
Hecang Zang[114] | 2022 | Detect and count wheat ears | YOLOv5s | AP: 71.61% | |
Fengkui Zhao[115] | 2022 | Detect and count wheat ears | YOLOv4 | R2: 0.973 | |
sorghum | Zhe Lin[116] | 2020 | Detect and count sorghum spikes | U-Net | AP: 95.5% |
rice | Yixin Guo[117] | 2021 | Calculate Rice Seed Setting Rate (RSSR) | YOLO v4 | mAP: 99.43% |
Jingye Han[118] | 2022 | Estimate Rice Yield | CNN | R2: 0.646 | |
kiwifruit | Zhongxian Zhou[119] | 2020 | Count fruit quantity | MobileNetV2,InceptionV3 | TDR: 89.7% |
mango | Juntao Xiong[120] | 2020 | Detect and count Mango | YOLOv2 | error rate: 1.1% |
grape | Thiago T. Santos[121] | 2020 | Detect and count grape string | Mask R-CNN, YOLOv3 | F1-score: 0.91 |
Lei Shen[122] | 2023 | Detect and count grape string | YOLO v5s | mAP: 82.3% | |
Hubert Cecotti[123] | 2020 | Detect grape | Resnet | mAP: 99% | |
Fernando Palacios[124] | 2022 | Detect and count grapeberry quantity | SegNet, SVR | R2: 0.83 | |
Shan Chen[125] | 2021 | Segment grape skewer | PSPNet | PA: 95.73% | |
Shan Chen[126] | 2022 | Detect and count grape string | Object detection, CNN, Transformer | MAPE: 18% | |
Marco Sozzi[127] | 2022 | Detect and count grape string | YOLO | MAPE: 13.3% | |
Fernando Palacios[128] | 2020 | Detect and count grapevine flower | SegNet | R2: 0.70 | |
apple | Lijuan Sun[129] | 2022 | Detect and count apple | YOLOv5-PRE | mAP: 94.03% |
Orly Enrique Apolo-Apolo[130] | 2020 | Detect and count apple | Faster R-CNN | R2: 0.86 | |
weed | Longzhe Quan[132] | 2021 | Estimate aboveground fresh weight of weeds | YOLO-V4 | mAP: 75.34% |
capsicum | Taewon Moon[133] | 2022 | Estimate fresh weight and leaf area | ConvNet | R2: 0.95 |
soybean | Wei Lu[134] | 2022 | Predict soybean yield | YOLOv3, GRNN | mAP: 97.43% |
Luis G. Riera[135] | 2021 | Count soybean pods | RetinaNet | mAP: 0.71 |
Image types | Obtaining methods | Image preprocessing | Extracting indicators | Main advantages | Main Disadvantages | Representative algorithms |
---|---|---|---|---|---|---|
Remote sensing images | Low altitude drone: equipped with multispectral cameras, visible light cameras, thermal imaging cameras, and hyperspectral cameras | Size correction; Multi-channel image fusion; Projection conversion; Resampling; |
Surface reflectance; Multispectral vegetation index; Biophysical parameters; Growth environment parameters; |
Multi-channel image, containing time, space, temperature, and band information, multi-channel fusion, rich information | The spatiotemporal and band attributes are difficult to fully utilize, and the shooting distance is far, making it suitable for predicting the yield of large-scale land parcels with low accuracy; Easily affected by weather |
ML,ANN,CNN-LSTM,3DCNN |
Satellite | Low spatial and temporal resolution, long cycle time, and pixel mixing | |||||
Visible light images | Digital camera | Size adjustment; Rotation; Cropping; Gaussian blur; Color enhancement; Brightening; Noise reduction, etc; Annotation; Dataset partitioning; |
Color index; Texture index; Morphological index; |
Easy to obtain images at a low cost | Only three bands of red, green, and blue have limited information content | Linear regression, ML, |
YOLO, Resnet, SSD, Mask R-CNN |
Calculation method | Implementation method | advantage | disadvantages |
---|---|---|---|
Artificial field investigation | Manual statistical calculation by calculation tools | Low technical threshold, simple operation, and strong universality | Each step of the operation is cumbersome and prone to errors, and some crops are also subject to damage detection |
Meteorological model | Analyze the correlation of meteorological factors and establish models using statistical, simulation, and other methods | Strong regularity, and strong guiding significance for crop production | Need a large amount of historical data to accumulate, suitable for large-scale crops |
Growth model | Digging a large amount of growth data to digitally describe the entire growth cycle of crops | Strong mechanism, high interpretability, and high accuracy | The growth models have numerous parameters and are difficult to obtain, which are only suitable for specific varieties and regions, and their application is limited |
Remote sensing calculation | Obtaining remote sensing data from multiple channels such as multispectral and hyperspectral data to establish regression models | Expressing internal and external characteristics of crops, which can reflect agronomic traits of crops | Applicable to specific regions, environments, and large-scale crops |
Image detection | Implementing statistics and counting through target segmentation or detection | Low cost and high precision | A large number of sample images are required, and the occlusion problem is not easy to solve |
Classification | Variety | Crop characteristics | Yield calculation indicators |
---|---|---|---|
food crops | corn | Important grain crop with strong adaptability, planted in many countries, and also an important source of feed | Number of plants, empty stem rate, number of grains per spike |
wheat | The world's highest sowing area, yield, and distribution of food crops; High planting density and severe mutual obstruction | Number of ears, number of grains per ear, and thousand-grain weight | |
rice | One of the world's most important food crops, accounting for over 40% of the total global food production | Number of ears, number of grains per ear, seed setting rate, thousand-grain weight | |
economic crops | cotton | One of the world's important economic crops, important industrial raw materials and strategic supplies | Total number of cotton beads per unit area, number of cotton bolls per plant, and quality of seed cotton per boll |
soybean | One of the world's important economic crops, widely used in food, feed, and industrial raw materials | Number of pods, number of seeds per plant, and weight of 100 seeds | |
potato | Potatoes are the world's fourth largest food crop after wheat, corn, and rice | Tuber weight and fruiting rate | |
sugarcane | Important economic crops, grown globally, important sugar raw materials | Single stem weight and number of stems | |
sunflower | Important economic and oil crops | Kui disk size and number of seeds | |
tea | Important beverage raw materials | Number and density of tender leaves | |
apple | The third largest fruit crop in the world | Number of plants per mu, number of fruits per plant, and fruit weight | |
grape | Fruit consumption and brewing raw materials have high social and economic impacts | Grape bead count, ear count, and grain count | |
orange | The world's largest category of fruits has become a leading industry in many countries | Number of plants per mu, number of fruits per plant, and fruit weight | |
tomato | One of the main vegetable varieties in the facility, and also an important raw material for seasoning sauces | Number of spikes per plant, number of fruits, and fruit weight | |
almond | Common food and traditional Chinese medicine raw materials | Number of plants per mu, number of fruits per plant, and fruit weight | |
kiwifruit | One of the most consumed fruits in the world, renowned as the "King of Fruits" and "World Treasure Fruit" | Number of plants per mu, number of fruits per plant, and fruit weight |
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