Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants

Abstract

Among the technological tools used in precision agriculture, the convolutional neural network (CNN) has shown promise in determining the nutritional status of plants, reducing the time required to obtain results and optimizing the variable application rates of fertilizers. Not knowing the appropriate amount of nitrogen to apply can cause environmental damage and increase production costs; thus, technological tools are required that identify the plant’s real nutritional demands, and that are subject to evaluation and improvement, considering the variability of agricultural environments. The objective of this study was to evaluate and compare the performance of two convolutional neural networks in classifying leaf nitrogen in strawberry plants by using RGB images. The experiment was carried out in randomized blocks with three treatments (T1: 50%, T2: 100%, and T3: 150% of recommended nitrogen fertilization), two plots and five replications. The leaves were collected in the phenological phase of floral induction and digitized on a flatbed scanner; this was followed by processing and analysis of the models. ResNet-50 proved to be superior compared to the personalized CNN, achieving accuracy rates of 78% and 48% and AUC of 76%, respectively, increasing classification accuracy by 38.5%. The importance of this technique in different cultures and environments is highlighted to consolidate this approach.

Plant phenotyping relevance

RGB画像からイチゴ葉の窒素栄養状態を分類するCNNを比較評価しており、植物形質推定手法の技術的検証が中心です。

Code and data availability

The paper describes a strawberry leaf image bank (1500 blocks) and Matlab CNN scripts, but no public deposit, availability statement, or URL for the dataset, images, code, or trained models is provided. The only URL in the text is the EMBRAPA soil/plant chemical analysis manual, a cited methodological reference, not a

No evidence-backed public reproduction asset is currently recorded.