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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #390886

Research Project: Advancement of Sensing Technologies for Food Safety and Security Applications

Location: Environmental Microbial & Food Safety Laboratory

Title: Estimation of cold stress, plant age, and number of leaves in watermelon plants using image analysis

item NABWIRE, SHONA - Chungnam National University
item WAKHOLI, COLLINS - Chungnam National University
item FAQUURZADA, MOHAMMAD - Chungnam National University
item ARIEF, MUHAMMAD - Chungnam National University
item Kim, Moon
item BAEK, INSUCK - Orise Fellow
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/28/2022
Publication Date: 2/18/2022
Citation: Nabwire, S., Wakholi, C., Faquurzada, M., Arief, M., Kim, M.S., Baek, I., Cho, B. 2022. Estimation of cold stress, plant age, and number of leaves in watermelon plants using image analysis. Frontiers in Plant Science. 13:847225.

Interpretive Summary: Watermelon plants that are subjected to low temperatures during crop production can experience a variety of stress effects such as delayed flowering or loss of flowers, pollen sterility, distorted growth, and reduced fruit set, which can lead to low fruit yield and quality. Early detection of low-temperature stress in the plants can improve temperature management for controlled production environments and also be useful in analysis of phenotypic traits for watermelon cultivation. After germination from seed at 28C, four varieties of watermelon plants were then transplanted and transferred to growth chambers for weekly color imaging during five weeks of growth spanning the seedling to flowering stages during which low temperatures can have greatly detrimental effects on subsequent plant growth and fruiting. Day/night temperatures were 28C/21C for the control group and 15C/10C for the cold-stressed plants. Images were acquired both from overhead and from multiple side-views of the plants. Imaging-based prediction models were developed using and demonstrated 100% accuracy in distinguishing between normal and cold-stressed watermelon plants. These results can be implemented for color imaging-based high-throughput systems for plant growth monitoring by watermelon producers and cultivators to help them ensure effective environmental management for improving fruit yield and quality.

Technical Abstract: Watermelon (Citrullus lanatus) is a widely consumed, nutritious fruit. Due to the temperature sensitivity of watermelon plants, temperatures must be closely monitored and controlled when the crop is cultivated in controlled environments. Abiotic stresses caused by temperature changes are a significant challenge in crop production. Studies have found that immediate plant morphological responses to these stresses include reductions in leaf size, number of leaves, and plant size. Stress diagnosis based on morphological features is important for preemptive temperature control and phenomics studies. The purpose of this study is to classify watermelon plants exposed to low-temperature stress conditions from the non-stress group using morphological features extracted using image analysis. In addition, an attempt was made to develop a model for predicting the number of leaves and growth stages using morphological 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 growth stage 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.