Can digital technology promote sustainable agriculture? Empirical evidence from urban China

Liu, Liqun; Liu, Kai

doi:10.1080/23311932.2023.2282234

Cited by 7 publications

(3 citation statements)

References 86 publications

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“…Furthermore, incorporating digital technologies in agriculture can also improve the human capital of farmers by allowing them to access productivity-enhancing educational and training resources and expert advice through digital platforms. This can assist in improving their knowledge and acquiring new skills, which could engender more efficient and sustainable agricultural production [ 25 , 26 ]. Lastly, agricultural digitalization can also build the natural capital of smallholders by enabling them to deploy digital technologies in precision agriculture to ensure optimal and sustainable use of natural resources, as well as early detection of diseases, pests, or nutrient deficiencies in their crops.…”

Section: Conceptual Linkages Between Agricultural Digitalization and ...mentioning

confidence: 99%

Exploring the impact of agricultural digitalization on smallholder farmers' livelihoods in Ghana

Addison,

Bonuedi,

Arhin

et al. 2024

Heliyon

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Section: Conceptual Linkages Between Agricultural Digitalization and ...mentioning

confidence: 99%

Exploring the impact of agricultural digitalization on smallholder farmers' livelihoods in Ghana

Addison,

Bonuedi,

Arhin

et al. 2024

Heliyon

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“…Through precise targeted spraying, these enhancements have effectively reduced the amount of herbicides used. However, the use of herbicides cannot be completely eliminated, which contradicts the principles of sustainable green agriculture [15]. Laser-targeted weeding, as a branch of targeted weeding, has become feasible with the maturation of compact laser devices and the enhanced transferability of computer vision models.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv8-Seg Based on Multiscale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Lyu,

Lu,

2024

Applied Sciences

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Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8 MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, and is suitable for complex vegetable field environments and resource-limited laser weeding devices.

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“…IT can influence the TE of crop production by shaping farmers' production strategies concerning the amalgamation and application of diverse inputs, such as fertilizers, pesticides, labor, and capital assets. TE denotes the ratio of observed output to the maximum achievable output given the existing inputs Liu and Liu, 2023), reflecting the effectiveness with which various agricultural inputs are utilized. Existing literature indicates that the use of these modern technologies significantly impacts farmers' decisions regarding seed and fertilizer usage (Kaila and Tarp, 2019), and land expansion (Zheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Exploring the influence of internet technology adoption on the technical efficiency of food production: insight from wheat farmers

Ahmad,

Zhao,

Jile

et al. 2024

Front. Sustain. Food Syst.

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Digital technology plays a crucial role in advancing sustainable farming and ensuring food security, especially in developing countries. This study evaluates the impact of Internet technology usage on technical efficiency in crop productivity, using data from 600 wheat farmers in rural Pakistan. It addresses the imperative need to enhance agricultural practices within the context of sustainable food production. To achieve this, a matched sample of Internet users and non-users was formed through propensity score matching. The study employs the stochastic frontier method with sample selection adjustment, ensuring a robust evaluation of technical efficiency between these groups. The findings reveal a positive influence of Internet usage on efficiency, persisting even after mitigating self-selection bias from observed and unobserved factors. Internet users exhibit a technical efficiency score of 0.62, surpassing the 0.55 score of non-users. Quantile regression analysis exposes varying impacts of Internet usage on technical efficiency, with less efficient farmers experiencing substantial improvements. Widespread Internet adoption holds the potential to significantly enhance agricultural production for growers. The research underscores the role of promoting Internet utilization to stimulate growth and improve farming efficiency within the evolving digital economy. Policymakers are advised to promote the adoption of modern technology to enhance crop production and support economic growth.

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Can digital technology promote sustainable agriculture? Empirical evidence from urban China

Cited by 7 publications

References 86 publications

Exploring the impact of agricultural digitalization on smallholder farmers' livelihoods in Ghana

Exploring the impact of agricultural digitalization on smallholder farmers' livelihoods in Ghana

Improved YOLOv8-Seg Based on Multiscale Feature Fusion and Deformable Convolution for Weed Precision Segmentation

Exploring the influence of internet technology adoption on the technical efficiency of food production: insight from wheat farmers

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