Bespoke cultivation of seablite with digital agriculture and machine learning

• DOI: 10.1016/j.ecolind.2024.112559
• Publication Date: 2024-08-30T22:57:19Z
• Authors: Thanapong Chaichana; Graham Reeve; Brett Drury; Yasinee Chakrabandhu; Sutee Wangtueai; Sarat Yoowattana; Supot Sookpotharom; Nathaphon Boonnam; Charles S. Brennan; Jirapond Muangpra...
• Keywords: Digital Technology; Ecology; Sustainable agriculture; 01 natural sciences; Soil Model; Sustainable Agriculture; Machine Learning; Ecological Modelling; Soil model; Machine learning; Digital technology; QH540-549.5; Ecological modelling; 0105 earth and related environmental sciences

Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants direct gradients suited particular requirement specific plant cultivation sustainable agriculture. The purpose this study is investigate smart seablite system. A novel digital agricultural method was developed applied digitised cultivation. Machine learning used predict future growth conditions plants (seablites). identified illustrative maps origins, conceptual model, essential factors growing seablite, circuit cultivating data phases comprised data. findings indicate that: (1) An indicator soil salinity quantity sodium chloride extracted from sample indicating its origin determinants. (2) Saline soil, saline water, pH, moisture, temperature, sunlight are development. These dependent on climate were measured using (3) Digital circuits provide better understanding relationship between phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting seablites. Therefore, finding showed that development can be manipulated as key controllers in response planned. Basic digitisation aids migration. an important practice agroecosystems.