Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

Sumaiya Islam, Md Nasim Reza, Shahriar Ahmed, Samsuzzaman, Kyu-Ho Lee, Yeon Jin Cho, Dong Hee Noh and Sun-Ok Chung ()
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Sumaiya Islam: Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
Md Nasim Reza: Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
Shahriar Ahmed: Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
Samsuzzaman: Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
Kyu-Ho Lee: Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
Yeon Jin Cho: Jeonnam Agricultural Research and Extension Services, Naju 58213, Republic of Korea
Dong Hee Noh: Jeonbuk Regional Branch, Korea Electronics Technology Institute (KETI), Jeonju 54853, Republic of Korea
Sun-Ok Chung: Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea

Agriculture, 2024, vol. 14, issue 8, 1-25

Abstract: The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm −1 , and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm −1 . Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.

Keywords: precision agriculture; seedling health; nutrient stress symptom; nutrient deficiency; computer vision; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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