# Applications of Machine Learning Technology in Agricultural Data Mining
## 2 Overview of Published Articles  
This Special Issue entitled 'Applications of Machine Learning Technology in Agricultural Data Mining' has published six research papers and one perspective, which, together, contribute to advances that demonstrate the real-world value of ML and AI technologies in agriculture. These papers addressed various aspects of ML usage in agricultural data. [PERSON] et al. [15] delved into CNNs, aiming to interpret and validate the model's decision-making process in the context of cow identification, a task that requires distinguishing subtle visual features among animals with similar appearances. Their study contained 760 images of 168 Holstein breed dairy cows, taken in several farms, situated in Southern Bulgaria, aiming to create a model for the visual face recognition of farm animals (cows) that could be used in future applications to manage the health, welfare, and productivity of the animals at the herd and individual levels in real time. By analyzing these attributions, the authors reveal that when examples are classified successfully, CNNs focus on pertinent cow-specific features such as facial structure and coat patterns. The study also uncovers that in some instances, models rely more on background elements, such as fences, barn walls, or lighting, rather than on cows themselves. This unintended functionality indicates potential biases in training data and argues for careful dataset design and model validation. What sets this research apart is its commitment to transparency and interpretability. Their results are comparable to other research studies on image classification, where accuracies of 92.9% and 91.66% were reported when using artificial neural networks [16, 17]. By using explainable AI (XAI) techniques for livestock identification, the researchers set a benchmark for the responsible and successful application of AI in agriculture, concluding that performance metrics alone are not enough, and that these techniques are required to elucidate how an AI model makes predictions. [PERSON] et al. [18] introduced a novel approach that combines unmanned aerial vehicle (UAV) imagery with advanced ML to assess and enhance seeding precision, to achieve uniform plant spacing, a pivotal aspect for optimal corn yields. Conducted in Serbia, the research involved the capture of high-resolution aerial images of corn fields in V4 growth using a DJI Inspire 1 drone. Three approaches for plant detection were compared: template matching, unsupervised VARII-based segmentation, and Mask R-CNN deep learning network. The best performance was attained by the Mask R-CNN, which was able to identify 96.5% of the corn plants correctly, and with high accuracy, also replicate ground-truth spacing measurements. The study further tested the influence of seed vibration on seed planting accuracy. The data indicated that greater amplitudes of vibration translate into more misses at planting and correspond to faster seeding speeds in the majority of cases. The study provides evidence on why improved seeder designs or adaptation of the control for speed that mitigates its impacts are critical. This study reveals that the integration of UAV imaging and AI analysis offers a potential solution to enhance seeding accuracy, enhance plant density optimization, and ultimately enhance corn production efficiency, which aligns with other previous reports [19]. In crop production, moisture prediction means better energy management, fewer quality losses, and greater sustainability in grain processing, factors with great importance for engineers, farmers, processors, and consumers alike. [PERSON] et al. [20] present a refined approach to predicting the moisture content of corn through the integration of ML and advanced variable selections, a key factor in ensuring optimal drying performance.