Abstract
Watermelon (Citrullus lanatus) is a widely consumed, nutritious fruit, rich in water and sugars. In most crops, abiotic stresses caused by changes in temperature, moisture, etc., are a significant challenge during production. Due to the temperature sensitivity of watermelon plants, temperatures must be closely monitored and controlled when the crop is cultivated in controlled environments. Studies have found direct responses to these stresses include reductions in leaf size, number of leaves, and plant size. Stress diagnosis based on plant morphological features (e.g., shape, color, and texture) is important for phenomics studies. The purpose of this study is to classify watermelon plants exposed to low-temperature stress conditions from the normal ones using features extracted using image analysis. In addition, an attempt was made to develop a model for estimating the number of leaves and plant age (in weeks) using the extracted features. A model was developed that can classify normal and low-temperature stress watermelon plants with 100% accuracy. The R2, RMSE, and mean absolute difference (MAD) of the predictive model for the number of leaves were 0.94, 0.87, and 0.88, respectively, and the R2 and RMSE of the model for estimating the plant age were 0.92 and 0.29 weeks, respectively. The models developed in this study can be utilized in high-throughput phenotyping systems for growth monitoring and analysis of phenotypic traits during watermelon cultivation.
Highlights
Watermelon (Citrullus lanatus) is a highly nutritious fruit comprised of 93% water with small quantities of protein, fat, minerals, and vitamins
The images were later fed into an image analysis pipeline to estimate the phenotypic traits of the watermelon plants
The classification model achieved a test accuracy of 100% while using features from two and three different view images, indicating a minimum requirement of two images for 100% classification
Summary
Watermelon (Citrullus lanatus) is a highly nutritious fruit comprised of 93% water with small quantities of protein, fat, minerals, and vitamins. It is widely considered a functional food, contributing to its widespread consumption around the world (Assefa et al, 2020). The less-than-pristine performance of the conventional image processing-based algorithm was due to of the poorly handled variances that existed in the data caused by inter-image intensity differences due to sample color/intensity variances. From these results, U-Net background removal was used for segmenting the watermelon plant from the background scene.
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