Advances in crop phenotyping and multi-environment trials

Liu, Zhe; Zhang, Fan; Ma, Qin; An, Dong; Li, Lin; Zhang, Xiaodong; Zhu, Ding; Li, Shaoming

doi:10.15302/j-fase-2015051

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…Agricultural information at the farmland scale can be directly applied Sensors 2021, 21, 1994 2 of 22 to the optimization of cultivation management and the analysis of breeding decisions. It has further applications compared with the large-scale agricultural monitoring technology used for macro decision-making [5]. Moreover, the acquisition of farmland-scale agricultural information places more stringent requirements on image data timeliness and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Han

Huang

et al. 2021

Sensors

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This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

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Section: Introductionmentioning

confidence: 99%

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Han

Huang

et al. 2021

Sensors

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“…Under three conditions, Liu et al [46] examined crop phenotyping. The first was in managed conditions, for example, green houses or specially built platforms, using high-throughput phenotyping techniques.…”

Section: Maximize the Outputmentioning

confidence: 99%

The Prospect of Artificial Intelligence (AI) in Precision Agriculture for Farming Systems Productivity in Sub-Tropical India: A Review

Naresh

Chandra

Vivek

et al. 2020

CJAST

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Agriculture is becoming more integrated in the agro-food chain and the global market, while environmental, food safety and quality are also increasingly impacting on the sector. It is facing with new challenges to meet growing demands for food, to be internationally competitive and to produce agricultural products of high quality. To cope with these challenges, Agriculture requires a continuous and sustainable increase in productivity and efficiency on all levels of agricultural production, while resources like water, energy, fertilizers etc. need to be used carefully and efficiently in order to protect and maintain the soil quality and environment. Consequently, Agriculture needs help in handling the complexity, uncertainty and fuzziness inherent in this domain. It requires new solutions for all aspects of agricultural farming, including precision farming and optimized resource application. Artificial Intelligence (AI) technology helps various industries to improve production and productivity. In agriculture, AI also allows farmers to increase their productivity and reduce negative environmental impacts. AI is changing the way our food is processed, where emissions from the agricultural sector have decreased by 20%. Together with precision agriculture (PA) and other emerging technologies, artificial intelligence (AI) can play a key role in modernizing agricultural practices and achieving the goal of improving the productivity of alternative arable cropping systems. In offering progressive change with advanced approaches, AI's future in agriculture is well ahead. The aim of this paper is to review various agricultural intelligence applications and to reduce the use of colossal amounts of chemicals with the aid of these technologies, resulting in reduced spending, improved soil fertility and increased productivity. With AI tools and machine learning, farmers can improve yields, protect their crops and have a much more reliable source of food.

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“…The latter can be determined in different ways, however, the most diverse approaches regard the application of the fast chlorophyll fluorescence or plant efficiency analyzer (PEA) and the pulse amplitude modulation (PAM) fluorimetry [44]. Fluorescence analysis can be utilized in phenotyping of vegetable crops to asses biotic and abiotic stresses [45], tissue chemical composition and characterization [54], and different plant physiological conditions [14,49,55]. Furthermore, chlorophyll fluorescence imaging (CFI) is a step forward in fluorescence analysis, accomplished by the support of CCD cameras.…”

Section: Fluorescencementioning

confidence: 99%

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Tripodi¹,

Massa²,

Venezia³

et al. 2018

Agronomy

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Abstract:Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with "omics" disciplines will also be discussed.

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Advances in crop phenotyping and multi-environment trials

Cited by 7 publications

References 37 publications

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

The Prospect of Artificial Intelligence (AI) in Precision Agriculture for Farming Systems Productivity in Sub-Tropical India: A Review

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

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