Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

Li, Changchun; Ma, Chao; Pei, Haojie; Feng, Haikuan; Shi, Jinjin; Wang, Yilin; Chen, Weinan; Li, Yacong; Feng, Xia‐Ting; Shi, Yonglei

doi:10.17265/2161-6264/2020.04.001

Cited by 3 publications

(2 citation statements)

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“…Similarly, [43] estimated potato biomass and yield using machine learning and remote The use of tools that efficiently and accurately estimate strawberry production enables resource planning for the harvesting, transport, and commercialization of the product. It also lowers labor costs, since this estimation process is carried out manually, which is lengthy, laborious, and subjective.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Strawberry Crop Productivity by Machine Learning Algorithms Using Data from Multispectral Images

et al. 2023

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Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA). The experiment, which was conducted in the experimental area of the Botany Laboratory at the Federal University of Uberlândia-Monte Carmelo Campus (Universidade Federal de Uberlândia, Campus Monte Carmelo), was carried out using a randomized block design with six treatments and four replications. The treatments comprised six commercial strawberry varieties: San Andreas, Albion, PR, Festival, Oso Grande, and Guarani. Images were acquired on a weekly basis and then preprocessed to extract radiometric values for each plant in the experimental area. These values were then used to train the production prediction algorithms. During the same period, data on the average fruit weight, number of fruits per plant, and number of leaves were collected. The total fruit weight in the field was 48.08 kg, while the linear regression (LR) and Support Vector Machine (SVM) estimates were 48.04 and 43.09 kg, respectively. The number of fruits obtained in the field was 4585, and the number estimated by LR and SVM algorithms was 4564 and 3863, respectively. The number of leaves obtained in the field was 10,366, and LR and SVM estimated 10,360 and 10,171, respectively. It was concluded that LR and SVM can estimate strawberry production and the number of fruits and leaves using multispectral unmanned aerial vehicle (UAV) images. The LR algorithm was the most efficient in estimating production, with 99.91% accuracy for average fruit weight, 99.55% for the number of fruits and 99.94% for the number of leaves. SVM exhibited 89.62% accuracy for average fruit weight, 84.26% for the number of fruits, and 98.12% for the number of leaves.

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Section: Discussionmentioning

confidence: 99%

“…Similarly, ref. [43] estimated potato biomass and yield using machine learning and remote sensing, obtaining estimated biomass (RMSE = 5.8) and yield (RMSE < 20%), and the better analysis method was RL, which was more accurate in estimating the tubercle formation phase.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Strawberry Crop Productivity by Machine Learning Algorithms Using Data from Multispectral Images

et al. 2023

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A Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing

Mukiibi,

Machakaire,

Franke

et al. 2024

Potato Res.

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Crop intelligence and yield prediction of potato (Solanum tuberosum L.) are important to farmers and the processing industry. Remote sensing can provide timely information on growth status and accurate yield predictions during the growing season. However, there is limited documentation on the most suitable vegetation indices (VIs) and optimal growth stages for acquiring remote sensing imagery of potato. To address this knowledge gap, a systematic review was conducted. Original scientific manuscripts published between 2000 and 2022 were identified using various databases. The findings indicate that satellite imagery is the most widely used source of remote sensing data for tuber yield prediction, whereas unmanned aerial vehicle systems (UAVs) and handheld sensors are more frequently applied for growth monitoring. The normalized difference vegetation index (NDVI), red-edge chlorophyll index (CIred-edge), green chlorophyll index (CIgreen), and optimized soil-adjusted vegetation index (OSAVI) are the most frequently used VIs for the growth and yield estimation of potato. The tuber initiation stage was found to be the most appropriate stage for remote sensing data acquisition. This review will assist potato farmers, agronomists and researchers in selecting the most suitable VIs for monitoring specific growth variables and selecting the optimal timing during the growing season to obtain remote sensing images.

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Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

Lima

Burnside

et al. 2022

Remote Sensing

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The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.

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Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

Cited by 3 publications

References 0 publications

Estimation of Strawberry Crop Productivity by Machine Learning Algorithms Using Data from Multispectral Images

Estimation of Strawberry Crop Productivity by Machine Learning Algorithms Using Data from Multispectral Images

A Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

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