Computer vision and deep learning for climate-resilient agriculture: Improving soil health, crop protection, and community livelihoods
Keywords:
Agriculture, Climate Resilience, Computer Vision, Deep Learning, Crop Protection, Soil HealthAbstract
Agricultural productivity and rural livelihoods are under threat from climate change, soil degradation, crop diseases and outbreaks of pests, and intelligent and resilient farming systems are needed. The study focused on the potential use of computer vision and deep learning to enhance soil health monitoring, protection of crops, adaptation to climate change and community livelihoods. The design was quantitative with the use of a structured questionnaire with 5-point likert scale for data collection, from a sample of 300 farmers and agricultural stakeholders. Descriptive statistics, reliability analysis, Pearson correlation, and multiple regression were applied to analyze the data. The findings showed high mean scores for computer vision (M = 4.18, SD = 0.62), deep learning (M = 4.24, SD = 0.59), soil health (M = 4.07, SD = 0.67), crop protection (M = 4.31, SD = 0.55), climate resilience (M = 4.16, SD = 0.61), and community livelihoods (M = 4.09, SD = 0.64). The analyses showed that computer vision (β = 0.39, p < 0.001) and deep learning (β = 0.48, p < 0.001) significantly predicted the variance of climate resilience (62%). The predictors accounted for 67% of variance in crop protection, and the deep learning had the larger contribution (β = 0.52, p < 0.001). The research demonstrated that incorporating computer vision and deep learning can boost agricultural monitoring, protection, climate adaptation, and livelihood resilience. The results suggest investment in affordable, farmers tailored, locally adaptable AI solutions for sustainable agriculture.
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Copyright (c) 2026 Basit Amin, Jawaria Nasir, Dr. Imran Khan, Hussain Shah (Author)
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