Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Bauer, Alan; Bostrom, Aaron; Ball, Joshua; Applegate, Christopher; Cheng, Tao; Laycock, Stephen D.; Rojas, Sergio Moreno; Kirwan, Jacob; Zhou, Ji

doi:10.1038/s41438-019-0151-5

Cited by 127 publications

(67 citation statements)

References 43 publications

(35 reference statements)

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“…With Allocation of crops to be cultivated in the agricultural land is done by utilizing the soil / weather conditions. Crop is allocated in the particular region in such a way that no adjacent land region have same crop to be cultivated Notify suggestions to the farmer to cultivate crop [23]. Maximizing the profit is not only the motive for farmers.…”

Section: Discussionmentioning

confidence: 99%

A novel approach for agricultural decision making using graph coloring

2019

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This article presents a novel approach for agricultural decision making using graph coloring algorithm. Farmers' welfare depends on the Agricultural production and balancing the demand so that they get more profit in crop cultivation. If production increases, demand decreases. If demand decreases, profit in crop cultivation decreases (loss in crop cultivation many times). If more crops of same kind are cultivated by the farmers in the nearby area, demand of the particular crop decreased. Farmers will receive less profit if demand is decreased which will decrease the cost of yields. To evade this problem, a novel approach is devised to give better yield production, balancing the demand as well as to make the farmer to earn more amount of money. Graph Coloring algorithm is used to assist the farmer that what crop to be cultivated without loss of investing the amount in crop cultivation as well as balancing the demand of the crop. Experimental results show that proposed approach outperforms well in terms of running time efficiency.

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Section: Discussionmentioning

confidence: 99%

A novel approach for agricultural decision making using graph coloring

2019

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“…Future work will introduce the Thin-Plate Spline interpolation algorithm to calibrate colors in sRGB space [53], the depth camera [54] and advance artificial intelligent algorithms [55,56] for improving navigation precision, speed and robust during tractor tillage operation.…”

Section: Discussionmentioning

confidence: 99%

Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

Lu¹,

Zeng²,

Wang³

et al. 2019

Preprint

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An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and it’s application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can effectively distinguish the boundary between the new and old soil than other competing vanilla methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering inspite of having the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new and old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

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“…Remote Sensing with UAV: The Further You Look, the More You See Drones (or UAVs) provide a flexible platform, quickly acquiring data over large areas and potentially providing high spatial resolution images ($1 mm per pixel). Some advanced IT techniques, such as deep learning (DL), can handle of millions of remote sensing images with high accuracy and high speed (Bauer et al, 2019). Thus, remote sensing has been widely used to monitor drought stress response, assess nutrient status and growth, detect weeds and pathogens, predict yield (Maes and Steppe, 2019), and identify QTLs (Wang et al, 2019).…”

Section: Ground-based Phenotyping: High Diversity Of Phenotyping Solumentioning

confidence: 99%

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

et al. 2020

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Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

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Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production

Cited by 127 publications

References 43 publications

A novel approach for agricultural decision making using graph coloring

A novel approach for agricultural decision making using graph coloring

Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

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