Automated Tenderness Assessment of Okra Using Robotic Non-Destructive Sensing

The quality of okra is crucial in satisfying consumer expectations, and the tenderness of okra is an essential parameter in estimating its condition. However, the current methods for assessing okra tenderness are slow and prone to errors, necessitating the development of a better, non-destructive method. The objective of the present study is to develop and test a non-destructive robotic sensor for assessing okra freshness and tenderness. A total of 120 pods were divided into two sets and stored under different conditions: 60 pods were kept in a cold chamber for 24 h (considered tender), while the other 60 pods were stored at room temperature for two days. First, the samples were assessed non-destructively using the force sensor of a collaborative robot, where a jamming pad (with internal granular fill) was capable of adapting and copying the okra shapes while controlling its force deformation. Second, the okra pods were evaluated with the referenced destructive tests, as well as weight loss, compression, and puncture tests. In order to validate the differences in the tenderness of the two sets, a discriminant analysis was carried out to segregate the okra pods into the two categories according to the destructive variables, confirming the procedure which was followed to produce tender and non-tender okra pods. After the differences in the tenderness of the two sets were confirmed, the variables extracted from the robotic sensor (maximum force (F_max), first slope (S₁), second slope (S₂), the first overshoot (O_s), and the steady state (S_s)) were significant predictors for the separation in the two quality categories. Discriminant analysis and logistic regression methods were applied to classify the pods into the two tenderness categories. Promising results were obtained using neural network classification with 80% accuracy in predicting tenderness from the sensor data, and a 95.5% accuracy rate was achieved in distinguishing between tender and non-tender okra pods in the validation data set. The use of the robotic sensor could be an efficient tool in evaluating the quality of okra. This process may lead to substantial savings and waste reduction, particularly considering the elevated cost and challenges associated with transporting perishable vegetables.