Maturity classification using mechanical characteristics of hydroponic lettuce

Accurate identification of lettuce maturity is one of the most important steps during vegetable production. The better quality of lettuce can depend mainly on the picking time in the period of suitable maturity. Among them, the harvested lettuce texture is one of the important bases to determine lettuce maturity in this case. Fortunately, the mechanical characteristics can also be an optimal way to evaluate and detect the lettuce texture after harvesting. It is very necessary to clarify the relationship between the lettuce maturity and harvested texture. In this study, a novel classification was proposed to characterize the maturity of vegetables, according to the mechanical characteristics of hydroponic lettuce under the puncture test. Three categories were also classified by the immature, mature, and over-mature plants. Firstly, a systematic extraction was performed on the mechanical characteristics of the stem and mesophyll in the inner, middle, and outer leaves of immature, mature, and over mature plants in the time and frequency domain. The correlation analysis was made between the leaf mechanical characteristics and chlorophyll content, and the above-ground fresh weight. Some indicators were then calculated to accurately characterize the leaf maturity characteristics. A consistency analysis was also implemented between the mechanical characteristics and the changes of soluble sugar or soluble protein. Secondly, a dual-threshold depth traversal was designed to classify the individual characteristics of a single leaf and single region. The classification thresholds of immature and mature were obtained for the mature and over-mature mechanical characteristics. An optimal selection was performed on the blade type and region with the highest classification accuracy. All the mechanical features of the region were then trained using various machine learnings. The test results showed that the mechanical characteristics of the leaves were significantly correlated with the above-ground fresh quality of lettuce. There was a significantly low correlation with the Soil Plant Analysis Development (SPAD) value of the middle leaves, indicating the better consistence with the change law of soluble sugar and soluble protein. It infers that the mechanical characteristics of leaves can be expected to characterize the maturity of lettuce. The overall accuracy rate of the mechanical features in the single threshold classification was ranked in the descending order of: middle leaf > inner leaf > outer leaf. More importantly, the classification effect of each feature in the stem part was outstandingly better than that of the mesophyll part. Among them, the highest classification accuracy was 75.5% in the fracture force of the middle leaf stem. Specifically, the classification thresholds of fracture force were 0.98, and 1.38 N, respectively, for the immature and mature, while the mature and overripe plants. An optimal ensemble was achieved to better classify all the mechanical characteristics of the stem and mesophyll in the middle leaves. Correspondingly, the integration method was the Bagging, and the training learner was a decision tree. The accuracy rate was more than 94.3% for the three types of lettuce. A series of experiments were carried out to verify under the same conditions. Another batch of lettuce was planted in this case. The integrated model was also optimized using machine learning after training. The prediction accuracy rate was obtained by 91.7%, indicating the better validity of the improved model. Consequently, the mechanical characteristics can be expected to serve as a new tool for the rapid identification and accurate classification of vegetable maturity. An optimal harvest texture of mature lettuce was also determined to improve the harvest quality of lettuce.