The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.

A method is proposed for determining the glucose concentration on the human fingertip by extracting two optical parameters, namely the optical rotation angle and the depolarization index, using a Mueller optical coherence tomography technique and a genetic algorithm. The feasibility of the proposed method is demonstrated by measuring the optical rotation angle and depolarization index of aqueous glucose solutions with low and high scattering, respectively. It is shown that for both solutions, the optical rotation angle and depolarization index vary approximately linearly with the glucose concentration. As a result, the ability of the proposed method to obtain the glucose concentration by means of just two optical parameters is confirmed. The practical applicability of the proposed technique is demonstrated by measuring the optical rotation angle and depolarization index on the human fingertip of healthy volunteers under various glucose conditions.

According to the International Agency for Research on Cancer, there were 300,000 new cases of melanoma and over 1,000,000 new cases of non-melanoma skin cancer in 2018.^1,2 Furthermore, the true number of skin cancer cases may be even higher than this figure due to many factors such as the registration methodology of skin cancer, the quality of skin cancer data.³ Without early detection and preventative control, melanoma can quickly develop and become far riskier, e.g., from stage I with a five-year survival rate of 97% to stage IV with a five-year survival rate of just 20% to 10%.³ Therefore, the early detection of skin cancer is essential in improving the prognosis of skin cancer patients.

A surface plasmon resonance (SPR) prism coupler is proposed for the high-resolution non-invasive (NI) measurement of the circular birefringence (CB) properties of turbid media. The feasibility of the proposed device is demonstrated by means of numerical simulations. It is shown that the SPR sensor enables the CB properties to be detected with a resolution of up to 8.9 × 10⁻⁷ RIU (refractive index units) for refractive indices in the range of 1.3∼1.4. Moreover, for tissue phantom solutions containing 2% lipofundin, the device has a detection limit of 3.72 mg/dL. This resolution performance satisfies the detection limit of 10 mg/dL stipulated by the U.S FDA for point-of-care glucose monitoring devices. Thus, the proposed SPR sensor has significant potential for NI glucose sensing in such applications as diabetes detection and management.