Assessment of vegetation Índices derived from UAV images for predicting biometric variables in bean during ripening stage

Quille-Mamani, Javier; Porras-Jorge, Rossana; Saravia-Navarro, David; Valqui-Valqui, Lamberto; Herrera, Jordán; Chávez-Galarza, Júlio; Arbizu, Carlos I.

doi:10.4067/s0718-34292022000200039

Cited by 3 publications

References 18 publications

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Bean yield estimation using unmanned aerial vehicle imagery

Gomes Campos,

Nogueira Martins

2024

Braz J Biosyst Eng

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The common bean is a crop of substantial socioeconomic importance that is cultivated throughout the Brazilian territory. Despite that, studies conducted so far have shown limitations in the methodologies used for yield estimation. In this sense, emerging technologies such as unmanned aerial vehicles (UAVs) can help both in crop monitoring and in assessing crop yield. Therefore, this study aimed: (1) to estimate the bean yield using spectral variables derived from UAV imagery and (2) to define the best vegetative stage for yield estimation. For this, data from a field experiment were used. The beans were planted in a conventional system in an area of 600 m² (20 x 30 m). During the crop cycle, six flights were carried out using a UAV equipped with a five-band multispectral camera (Red, Green, Blue, Red Edge, and Near-infrared). After that, 10 spectral variables composed of the bands and five vegetation indices (VIs) were obtained. At the end of the season, the area was harvested, and the yield (kg ha-1) was determined. Then, the data was submitted to correlation (r), and regression analysis. Overall, all developed models showed moderate performance, but in accordance with the literature, with R² and RMSE values ranging from 0.52 to 0.57 and from 252.79 to 208.84 kg ha-1, respectively. Regarding the best vegetative stage for yield estimation, the selected models used data from the second flight (52 days after planting) at the beginning of pod formation and filling (between stages R7 and R8).

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Bean yield estimation using unmanned aerial vehicle imagery

Gomes Campos,

Nogueira Martins

2024

Braz J Biosyst Eng

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Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model

El-baki,

Ibrahim,

Elsayed

et al. 2024

Preprint

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Water stress is a global challenge that severely impacts crop production by hindering essential processes such as nutrient uptake, photosynthesis, and respiration. To address this issue, proximal sensing has emerged as a promising technique for detecting stress in plants. By utilizing remote sensing and non-destructive methods, early and spatial identification of stress in vegetable crops becomes possible, enabling timely management interventions and optimizing yield in precision farming. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels, growth stages, and growing seasons. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to decreased FB, DB, CWC, SMC, and tomato yield. The study also revealed significant differences in RGB image indices between different irrigation levels, with lower values observed under severe stress treatment. The majority of RGB image indices incorporating the green component demonstrated strong positive relationships, with R2 ranging between 0.52 and 0.94 for FB, 0.49 and 0.92 for DB, 0.44 and 0.85 for CWC, 0.29 and 0.82 for SPAD, 0.27 and 0.74 for SMC, and 0.42 and 0.89 for tomato yield. Notably, we did not observe a significant correlation between any of the RGB image indices and SPAD during the combined data of both stages. However, the red-blue simple ratio (RB) index, which does not consider the green component (G), did not significantly correlate with any of the plant traits. The ANN models utilizing RGB image indices achieved high prediction accuracy, as indicated by R2 values ranging from 0.84 to 0.99 for FB, 0.88 to 0.98 for DB, 0.81 to 0.97 for CWC, 0.67 to 0.98 for SPAD, 0.55 to 0.81 for SMC, and 0.83 to 0.96 for tomato yield. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under conditions of limited water availability for irrigation.

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Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru

Saravia

Valqui-Valqui

Salazar

et al. 2023

Drones

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In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.

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Assessment of vegetation Índices derived from UAV images for predicting biometric variables in bean during ripening stage

Cited by 3 publications

References 18 publications

Bean yield estimation using unmanned aerial vehicle imagery

Bean yield estimation using unmanned aerial vehicle imagery

Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model

Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru

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