This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P₂O₅-77.5, K₂O-0; Balance: N-135.1, P₂O₅-91, K₂O-0; Genezis: N-135, P₂O₅-75, K₂O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.

If we want to increase the efficiency of precision technologies to create sustainable agriculture, we need to put developments and their application on a new footing; moreover, a general paradigm shift is needed. There is a need to rethink close-at-hand and far-off innovation concepts to further develop precision agriculture, from both an agricultural, landscape, and natural ecosystem sustainability perspective. With this, unnecessary or misdirected developments and innovation chains can be largely avoided. The efficiency of the agrotechnology and the accuracy of yield prediction can be ensured by continuously re-planning during the growing season according to changing conditions (e.g., meteorological) and growing dataset. The aim of the paper is to develop a comprehensive, thought-provoking picture of the potential application of new technologies that can be used in agriculture, primarily in precision technology-based arable field crop production, which emphasizes the importance of continuous analysis and optimisation between the production unit and its environment. It should also be noted that the new system contributes to reconciling agricultural productivity and environmental integrity. The study also presents research results that in many respects bring fundamental changes in technical and technological development in field production. The authors believe that treating the subsystems of agriculture, landscape, and natural ecosystem (ALNE) as an integrated unit will create a new academic interdisciplinarity. ICT, emphasizing WSN (Wireless Sensor Network), remote sensing, cloud computing, AI (Artificial Intelligence), economics, sociology, ethics, and the cooperation with young students in education can play a significant role in research. This study treats these disciplines according to sustainability criteria. The goal is to help management fulfil the most important expectation of reducing the vulnerability of the natural ecosystem. The authors believe that this article may be one of the starting points for a new interdisciplinarity, ALNE.

Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis was that in drone imagery captured at altitudes of 20–30 m—where individual plant details are not discernible—weed presence among crops can be statistically detected, allowing for the generation of a weed distribution map. This study proposes a computer vision detection method using images captured by unmanned aerial vehicles (UAVs) consisting of six main phases. The method was tested on 208 images. The algorithm performs well under normal conditions; however, when the weed density is too high, it fails to detect the row direction properly and begins processing misleading data. To investigate these cases, 120 artificial datasets were created with varying parameters, and the scenarios were analyzed. It was found that a rate variable—in-row concentration ratio (IRCR)—can be used to determine whether the result is valid (usable) or invalid (to be discarded). The F1 score is a metric combining precision and recall using a harmonic mean, where “1” indicates that precision and recall are equally weighted, i.e., β = 1 in the general Fβ formula. In the case of moderate weed infestation, where 678 crop plants and 600 weeds were present, the algorithm achieved an F1 score of 86.32% in plant classification, even with a 4% row disturbance level. Furthermore, IRCR also indicates the level of weed pressure in the area. The correlation between the ground truth weed-to-crop ratio and the weed/crop classification rate produced by the algorithm is 98–99%. As a result, the algorithm is capable of filtering out heavily infested areas that require full weed control and capable of generating weed density maps on other cases to support precision weed management.

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment.