In this study, we experimentally analyzed whether people can perceive the accuracy or smartness of AI judgments, and whether the judgment accuracy of AI and the level of confidence in those judgments affect people’s decision-making. The results showed that people may perceive an AI’s smartness even when it only presents information on the results of its judgments. The results also suggest that AI accuracy and confidence affect human decision-making, and that the magnitude of the effect of AI confidence varies with AI accuracy. We also found that when a person’s ability to make a decision is less than or equal to the AI’s ability to make a decision, the human performance in a binary decision task improves regardless of AI accuracy. The results obtained in this study are similar in some respects to relationships in which people make decisions while interacting, and the findings from research on human interactions may apply to the research and development of human–AI interactions.

The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D², and D³, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D² and D³ methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D² and D³ methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

Appearances of products are important to companies as they reflect the quality of their manufacture to customers. Nowadays, visual inspection is conducted by human inspectors. This research attempts to automate this process using Convolutional AutoEncoders (CAE). Our models were trained using images of non-defective parts. Previous research on autoencoders has reported that the accuracy of image regeneration can be improved by adding noise to the training dataset, but no extensive analyse of the noise factor has been done. Therefore, our method compares the effects of two different noise patterns on the models efficiency: Gaussian noise and noise made of a known structure. The test datasets were comprised of “defective” parts. Over the experiments, it has mostly been observed that the precision of the CAE sharpened when using noisy data during the training phases. The best results were obtained with structural noise, made of defined shapes randomly corrupting training data. Furthermore, the models were able to process test data that had slightly different positions and rotations compared to the ones found in the training dataset. However, shortcomings appeared when “regular” spots (in the training data) and “defective” spots (in the test data) partially, or totally, overlapped.

Continuous vegetation monitoring is essential for predicting crop varieties and yields; however, optical satellite data are frequently unavailable due to cloud cover. To overcome this limitation, this study proposes a method for generating pseudo-NDVI (Normalized Difference Vegetation Index) imagery from RVI (Radar Vegetation Index) derived from Synthetic Aperture Radar (SAR) data using Generative Adversarial Networks (GANs). Two architectures—pix2pixHD (supervised) and CycleGAN (unsupervised)—were evaluated using Sentinel-1 and Sentinel-2 data under identical conditions. By introducing RVI as an intermediate feature instead of directly converting SAR backscatter to NDVI, the proposed method enhanced physical interpretability and improved correlation with NDVI. Quantitative results show that pix2pix achieved higher accuracy (SSIM = 0.5667, PSNR = 22.24 dB, RMSE = 20.54) than CycleGAN (SSIM = 0.5240, PSNR = 19.54 dB, RMSE = 28.02), with further improvement when combining VV and VH polarization data. Although the absolute accuracy remains moderate, this approach enables continuous annual NDVI time series reconstruction for crop monitoring under persistent cloud conditions, demonstrating clear advantages over conventional direct SAR-to-NDVI conversion methods.