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Amaranth (Amaranthus spp.) is a significant leafy vegetable and cereal crop with high nutrient benefits that is widely consumed worldwide. To maximise its yield, farmers massively rely upon synthetic fertilisers to enhance the quality of the crop. However, this obsessive usage of inorganic fertiliser leads to severe ecosystem damage. For agricultural and ecological sustainability, it is essential to comprehend the process underlying this environmental degradation. This paper analyses the effect of inorganic fertilisers on the growth and yield of Amaranthus. By identifying Amaranthus's productivity and adaptability in different chemically treated soil conditions and automatically phenotyping its traits using image-based deep learning models, this study aims to determine the overuse of synthetic fertilisers. A comparative evaluation of different state-of-art CNN models was carried out, and the experimental result proves that DenseNet-121 could be a more appropriate learning algorithm for the proposed system with 84% accuracy. It is believed that the proposed deep learning based automated phenotyping framework could greatly assist farmers in understanding the actual requirement of soil, thus avoiding the residual impact of fertiliser abuse in the environment.

来源平台：INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING