Improved yield estimation with efficient decision power for multi-line processes

Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.

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An Adaptive Delay Model for Timing Yield Estimation under Wide-Voltage Range

Proceedings of the 26th Asia and South Pacific Design Automation Conference ◽

10.1145/3394885.3431581 ◽

2021 ◽

Author(s):

Hao Yan ◽

Xiao Shi ◽

Chengzhen Xuan ◽

Peng Cao ◽

Longxing Shi

Keyword(s):

Delay Model ◽

Yield Estimation ◽

Voltage Range

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Fruits yield estimation using Faster R-CNN with MIoU

Multimedia Tools and Applications ◽

10.1007/s11042-021-10704-7 ◽

2021 ◽

Author(s):

Santi Kumari Behera ◽

Amiya Kumar Rath ◽

Prabira Kumar Sethy

Keyword(s):

Yield Estimation

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Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽

10.3390/land10020223 ◽

2021 ◽

Vol 10 (2) ◽

pp. 223

Author(s):

Rubaiya Binte Mostafiz ◽

Ryozo Noguchi ◽

Tofael Ahamed

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Agricultural Land ◽

Expert Knowledge ◽

Vegetation Indices ◽

Land Suitability ◽

Assessment System ◽

Yield Estimation ◽

Suitability Assessment ◽

Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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DeepCorn: A semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.106874 ◽

2021 ◽

Vol 218 ◽

pp. 106874

Author(s):

Saeed Khaki ◽

Hieu Pham ◽

Ye Han ◽

Andy Kuhl ◽

Wade Kent ◽

...

Keyword(s):

Deep Learning ◽

High Throughput ◽

Learning Method ◽

Corn Kernel ◽

Yield Estimation

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Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽

10.1186/s13007-020-00693-3 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Shanjun Luo ◽

Yingbin He ◽

Qian Li ◽

Weihua Jiao ◽

Yaqiu Zhu ◽

...

Keyword(s):

Precision Agriculture ◽

Growth Stage ◽

Estimation Error ◽

Potato Yield ◽

Hyperspectral Data ◽

Single Stage ◽

Accurate Estimation ◽

Yield Estimation ◽

Area Index ◽

Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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