Identification of nutrient deficiency stress for iron, zinc and manganese in baby spinach using computer vision

作者：Maryam Nadafzadeh, Ahmad Banakar, Saman Abdanan Mehdizadeh, Saeid Minaei, Abdul Mounem Mouazen, Gerrit Hoogenboom · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101524 · 被引用次数：3 · 研究领域：Leaf Properties and Growth Measurement、Smart Agriculture and AI、Spectroscopy and Chemometric Analyses

• A computer vision system was developed to detect deficiencies of micronutrients including iron, zinc, and manganese in spinach. • Varying levels of iron, manganese, and zinc deficiencies (from 0 to 100% of recommended amounts) were detected as soon as the true leaves of spinach emerged. • A relationship exists between the levels of iron, zinc, and manganese in spinach and the color, morphological, and texture parameters extracted from plant image. • ANN was more effective in identifying varying levels of micronutrient deficiencies in spinach compared to other classification algorithms such as SVM, KNN, Naive Bayes, and Random Forest. • The proposed classification model demonstrated higher accuracy than the expert in distinguishing four levels of iron, zinc, and manganese deficiencies in spinach during the cultivation period. • The proposed method enables farmers to address nutritional deficiencies in spinach plants early, preventing a decline in product quality. In recent years, various methodologies have been developed to evaluate the quality of agricultural products. These technologies have facilitated the monitoring of plant nutritional needs, thereby reducing reliance on human judgment and enhancing intuitive decision-making. This study focuses on the cultivation of baby spinach plants in soilless beds to precisely control nutrient conditions. The objective was to investigate deficiencies for three key micronutrients, namely, iron (Fe), zinc (Zn), and manganese (Mn), at...