A Review of Deep Learning in Multiscale Agricultural Sensing

Wang, Dashuai; Cao, Wujing; Zhang, Fan; Li, Zhuolin; Xu, Sheng; Wang, Xinyu

doi:10.3390/rs14030559

Cited by 114 publications

(85 citation statements)

References 126 publications

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“…The image labeling step is thus a clear bottleneck here. This means existing pipelines to label the training images need to be strengthened to be more efficient, whether they use fully manual annotation or new techniques such as transfer learning (TL) or few-shot learning (FSL) [ 37 ]. The specificity of manual annotations in the agricultural world is, however, that in certain cases, the user needs to be highly skilled at the task at hand.…”

Section: Discussionmentioning

confidence: 99%

Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments

Rançon

Keresztes

Deshayes

et al. 2023

Sensors

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The potential of image proximal sensing for agricultural applications has been a prolific scientific subject in the recent literature. Its main appeal lies in the sensing of precise information about plant status, which is either harder or impossible to extract from lower-resolution downward-looking image sensors such as satellite or drone imagery. Yet, many theoretical and practical problems arise when dealing with proximal sensing, especially on perennial crops such as vineyards. Indeed, vineyards exhibit challenging physical obstacles and many degrees of variability in their layout. In this paper, we present the design of a mobile camera suited to vineyards and harsh experimental conditions, as well as the results and assessments of 8 years’ worth of studies using that camera. These projects ranged from in-field yield estimation (berry counting) to disease detection, providing new insights on typical viticulture problems that could also be generalized to orchard crops. Different recommendations are then provided using small case studies, such as the difficulties related to framing plots with different structures or the mounting of the sensor on a moving vehicle. While results stress the obvious importance and strong benefits of a thorough experimental design, they also indicate some inescapable pitfalls, illustrating the need for more robust image analysis algorithms and better databases. We believe sharing that experience with the scientific community can only benefit the future development of these innovative approaches.

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Section: Discussionmentioning

confidence: 99%

Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments

Rançon

Keresztes

Deshayes

et al. 2023

Sensors

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show abstract

“…Due to DL algorithms' capacity to extract high-level features from data, DL was chosen. This DL capacity outperforms other conventional Machine Learning techniques [11].…”

Section: Introductionmentioning

confidence: 89%

IoT and AI Based Smart Soil Quality Assessment for Data-Driven Irrigation and Fertilization

Nyakuri¹,

Bizimana²,

Bigirabagabo³

et al. 2022

AJCE

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Purpose: The rapidly growing demand for food due to rapid population growth in East Africa is one of the challenging issues and the sustainable way of tackling it, is to enhance the agriculture activities to satisfy the need of increasing farm productivity. However, the climate change, limited water resources and poor soil fertility reduces crops yields. In attempt to solve these challenges, Internet of thing (IoT) in conjunction with artificial intelligence (AI) techniques is increasingly being used in agriculture sector. This study investigates an integration of IoT and a deep learning (DL) driven solution for smart irrigation and fertigation by assessing soil nutrients and soil water content dynamics in Eastern province of Rwanda for optimization of these scare resources while increasing yields productivity. Methodology: The research data for analysis was collected from KABOKU-KAGITUMBA irrigation scheme, and data on soil moisture and soil nutrients was gathered over a six-month period from 36 sensor nodes that were installed in approximately 70 hectares with 6 pivots for irrigation. The collected data in real time by sensors was sent to an IoT platform and incorporated with the forecasted weather information there after a deep learning based model used to predict when to irrigate and when to fertigate and the notification sent to farmer with recommendations. The irrigation valves were automatically actuated based on the predictions. The study's main software tools for gathering, displaying, and analyzing real-time data streams were Things Speak, Tensor Flow Lite, and the Arduino Software (IDE). A prototype was finally implemented effectively. Findings: The resulting model showed that can perform well with an accuracy of 91.7% and it can work well when deployed in the remote area with minimum internet connection. Unique Contribution to Practice: since the currently technologies used in irrigation and fertilization are manual or based on threshold values for automatic irrigation, we recommend the implementation of this solution since it will guarantee data-driven farming, which will help to protect the environment and ensure the optimization use of water resources. Additionally, this will result in lower operating cost, which will raise earnings.

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“…Myroides sp. JIL321 is a salt-tolerant bacterium that enhanced the chlorophyll concentration considerably in rice ( Wang et al., 2022a ). The photosynthetic pigment content of P. indica inoculated rice seedlings rose dramatically under high-salt conditions.…”

Section: Rhizospheric Microbiome As a Salinity-alleviating Agentmentioning

confidence: 99%

Plant growth-promoting rhizobacteria: Salt stress alleviators to improve crop productivity for sustainable agriculture development

et al. 2023

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Soil salinity, a growing issue worldwide, is a detrimental consequence of the ever-changing climate, which has highlighted and worsened the conditions associated with damaged soil quality, reduced agricultural production, and decreasing land areas, thus resulting in an unsteady national economy. In this review, halo-tolerant plant growth-promoting rhizo-microbiomes (PGPRs) are evaluated in the salinity-affected agriculture as they serve as excellent agents in controlling various biotic–abiotic stresses and help in the augmentation of crop productivity. Integrated efforts of these effective microbes lighten the load of agro-chemicals on the environment while managing nutrient availability. PGPR-assisted modern agriculture practices have emerged as a green strategy to benefit sustainable farming without compromising the crop yield under salinity as well as salinity-affected supplementary stresses including increased temperature, drought, salinity, and potential invasive plant pathogenicity. PGPRs as bio-inoculants impart induced systemic tolerance (IST) to plants by the production of volatile organic compounds (VOCs), antioxidants, osmolytes, extracellular polymeric substances (EPS), phytohormones, and ACC-deaminase and recuperation of nutritional status and ionic homeostasis. Regulation of PGPR-induced signaling pathways such as MAPK and CDPK assists in salinity stress alleviation. The “Next Gen Agriculture” consists of the application of designer crop microbiomes through gene editing tools, for instance, CRISPR, and engineering of the metabolic pathways of the microbes so as to gain maximum plant resistance. The utilization of omics technologies over the traditional approaches can fulfill the criteria required to increase crop yields in a sustainable manner for feeding the burgeoning population and augment plant adaptability under climate change conditions, ultimately leading to improved vitality. Furthermore, constraints such as the crop specificity issue of PGPR, lack of acceptance by farmers, and legal regulatory aspects have been acknowledged while also discussing the future trends for product commercialization with the view of the changing climate.

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A Review of Deep Learning in Multiscale Agricultural Sensing

Cited by 114 publications

References 126 publications

Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments

Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments

IoT and AI Based Smart Soil Quality Assessment for Data-Driven Irrigation and Fertilization

Plant growth-promoting rhizobacteria: Salt stress alleviators to improve crop productivity for sustainable agriculture development

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