Correction: Rodrigues et al. Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest. Agriculture 2024, 14, 161

Text Correction

The authors have recognized several errors in the original publication [1] that they would like to correct. During the peer review stage, the authors needed to make multiple extensive revisions to the manuscript. Due to miscommunication between the authors, several versions of the manuscript were used at the same time. Because of this, the authors did not correct numerous mistakes still present throughout the whole manuscript. In addition to the remaining errors in the text, the authors would like to point out that there were two major omissions that need correction:

• Removal of the last paragraph in Section 2.3, which was left in the main text by mistake. It is an older version of Section 2.2.

“The references were chosen openly and in parallel by two co-authors concurring to the inclusion/exclusion criteria. Unique duplicates that did not meet the consolidation criteria were denied from empower examination. Unique duplicates were chosen that met all of the taking after criteria: (a) the hypothetical delineated a think approximately related to any more distant identifying or watching (grain, era and post-harvest); (b) at smallest one of the more distant recognizing or watching methodologies was related to many arrange of era or post-harvest. After the screening handle, the data was removed from the tables or works of the articles.”

• Extensive changes to Section 4.6, which led to a total revision of the whole Sub-section. Some of the examples are changes to the terms “insect” into “creepy crawly” and “put away” into “storage” which were used in the initial submission of the manuscript.

“As most grains are delivered at given times of the year and are still required by businesses all through the year, an expansive portion of the dried grains are put away. Figure 6 shows a framework for observing grain amid capacity and its advance over a long time. To protect stored grain, it is fundamental to keep the mass with a secure water substance of between 11 and 14% and an intergranular temperature underneath 22 °C (depending on the locale). This requires that air circulation and thermometry are working appropriately, checking the conditions of the grains entering the capacity storehouse and the intergranular viscosity. In addition to observing the temperature, measuring the intergranular relative viscosity is vital for determining the harmony dampness substance of the stored item and foreseeing respiratory issues. Given that grains are highly warm insulin, since the temperature of the grain mass is measured by sensors inside the silos, the thermometry framework frequently fails to distinguish item warming problems. Therefore, observing the overall CO₂ concentration inside the capacity storehouse could help identify air circulation control. At levels above 600 ppm CO₂, there is a chance that the item disintegrates. The combination of these factors indirectly characterizes the quality of the grain and makes it possible to anticipate conceivable dangers of item weakening (Figure 6).

Warm-storage grain attracts invasive insect pests. According to Badgujar et al. [86], checking for insects in stored items is a common technique for the post-harvest management of stored grains and grain-based items, which can ensure the quality of the item from harvest to the final customer. Current inspecting and observing strategies can be time-consuming, labor-intensive, and costly, and require experience in recognizing insect pests. Hence, Badgujar et al. [86] created an image-based identification system for common insects found in storage with deep learning strategies. Top-down pictures of common adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were obtained and analyzed. State-of-the-art convolutional neural network (CNN) models based on deep learning (ResNet-50, MobileNet-v2, DarkNet-53 and EfficientNet-b0) were prepared with a transfer learning approach to classify the insect species. All the models were able to accurately recognize the insect species with at least 96% precision and with few classification mistakes. One issue with trained CNNs is that they do not clarify the reasoning behind the classification and are referred to as “black boxes”. Subsequently, visualization strategies called Gradient-weighted Class Activation Mapping (Grad-CAM) were used to investigate the black box network. Grad-CAM uses heat maps to highlight the image features on which the network has focused in order to create insect species predictions. Grad-CAM confirms the network’s prediction and improves the network’s execution. This process contributed to the general objective of creating a camera-based framework to screen for insects in stored grain. The system could serve as a tool for warehouses and other food facilities to rapidly and precisely recognize insect species in stored items as well as be realized as part of real-time monitoring.

Neethirajan et al. [72] created a CO₂ sensor to remotely screen the quality of stored grain. The sensor was created by employing a conductive polyaniline boronic acid polymer as the electrically conductive region of the sensor. The created sensor measured CO₂ levels within the 380–2400 ppm range, recognized at varying temperatures (between 25 and 55 °C), which need water from the air to operate, permitting CO₂ to be recognized between 20 and 70% relative humidity. In addition to grain capacity, variables such as temperature and relative humidity, grain moisture level, and CO₂ and insect concentrations must be observed and controlled, continuously looking for the leading conditions for grain preservation.

The respiration rate over time was analyzed by Ubhi and Sadaka [73] using CO₂ concentration sensors. The authors reported that the best detection method employing a weight sensor was found to be solid and sensitive for measuring the respiration rate of grains within the parameters. Onibonoje et al. [74] considered a remote sensor organization framework to screen natural components (temperature, relative humidity, and light) that influence grain capacity. The sensors were conveyed in settled areas flawlessly disseminated in a grain capacity storehouse. Onibonoje et al. [74] detailed that the remote sensor organization framework created makes a difference in guaranteeing nourishment security. As previously mentioned, programmed capacity procedures are broadly utilized in capacity units to distinguish grain weakening by checking the temperature and relative humidity of the grain, the dampness of the grain, and the CO₂ concentration. Depending on the measure of the storehouse, one or more cables containing an arrangement of sensors are hung vertically. On each cable, the sensors are, as a rule, 1.2 m apart. The number of cables in a storehouse depends on variables such as the estimation of the storehouse (primarily its breadth), the climatic conditions of the locale, and the species of grain to be stored. One of the most important points of interest of this framework is the real-time observation of capacity parameters [80,86]. The commonplace dividing between sensors (1.2 m) within the cable is considered in spatial determination.

Asefi et al. [80] evaluated a recent substitute for this monitoring technique [80]. These authors used electromagnetic imaging to monitor grains kept in silos. Global sensitivity, the utilization of inexpensive electromagnetic radiation, and the capacity to produce images with high spatial resolution and without disturbing or interacting with grains are just a few benefits of using electromagnetic images [80]. Similar to this, but on a larger scale, Asefi et al. [80] and Gilmore et al. [81] also investigated the monitoring of grain storage conditions. Gilmore et al. [81] reported that an electromagnetic imaging system demonstrated the ability to identify a 25% moisture-containing deteriorating region in the grain mass in addition to a 15% moisture-containing grain mass. Therefore, it is economically feasible to monitor stored grain using systems based on electromagnetic imaging [81]. The temperature rise in the grain mass caused by insects near the grain can also be detected using this sensing technique. Using photos, this technology can monitor on even the smallest changes in the storage environment [82]. An inexpensive, low-power single board computer called Jetson Nano, a manual focus camera, and a trained deep learning model made up the fundamental insect detection system created by Mendoza et al. [87]. Using a real-time visual feed, the model was validated. The authors claim that effective insect control depends on the timely detection, classification, and monitoring of insect pests in grain warehouses and food facilities. The insect’s image is taken by the camera and sent to a Jetson Nano for processing. A deep learning model that has been trained to identify the types and abundances of insects is used by Jetson Nano. The detection results are shown on a monitor under three different lighting conditions: white LED light, yellow LED light, and no lighting conditions. The system was tested with various stored-grain insect pests and was able to detect and classify adult warehouse insects with an acceptable level of accuracy by comparing accuracy on the basis of light sources and F1 scores. The results show that the system is an automated insect detection solution that is both efficient and reasonably priced.

Gilmore et al. [81] demonstrated a breakthrough in the application of electromagnetic imaging technology. A three-dimensional electromagnetic imaging system for measuring the grain moisture content during storage was developed by these authors. Data from the three-point sensors built into the compartment were compared with the outcomes of the 3D image. The electromagnetic imaging system can track the loss of moisture during drying and storage, according to Gilmore et al. [83]. They also mentioned that the method could indicate when the grains had reached safe storage temperatures.

External factors, such as the presence of insects, in addition to climate factors, such as temperature and relative humidity, and intrinsic grain characteristics, such as moisture content and respiration rate, can lower grain quality standards [88,89]. In order to prevent, control, and monitor the presence of insects near grains, it is necessary. Consequently, a small device was developed by Reimer et al. [90] to track insect activity in grain samples. The sensor’s foundation is an active microwave cavity, as the authors have shown. Since the presence of insects is a drawback for industries looking for optimal grain storage conditions, the sensor created by Reimer et al. [90] may be used to track the population density of insects in stored grain. The presence of insects must be detected using these sensors.

Fumigation, or the process of applying phosphine to the grain, is one of the primary methods used for this. Upon observing the use of this method, Brabec et al. [78] assessed wireless phosphine sensors to track the gas used to fumigate grain that was kept in storage. The automated fumigation data, according to the authors, gave a thorough picture of the procedure. Those in charge of fumigation can use this information to more effectively assess the process and guarantee effective insect control. Wireless phosphine sensors, according to Brabec et al. [78], offer a practical way to monitor on fumigation treatments, giving more information on variations in phosphine concentration during treatments. Internet-based systems facilitate easy access to data for both active fumigation and treatment outcome summaries [78]. As a result, sensor-assisted hermetic vacuum storage has become a viable substitute for traditional methods [79]. Kumar et al. [79] used hermetic storage, eliminating the oxygen in the storage cell and detecting the grains using temperature, relative humidity, pressure, and CO₂ sensors in place of chemical agents to control insects in stored grains. According to Kumar et al. [79], a CO₂ sensor can show whether grains are free of insects. Thus, to indicate the quality of grain stored in an airtight system, a decision support system based on multiple sensors—such as temperature and relative humidity—in addition to a CO₂ sensor can be helpful. Furthermore, the authors noted that the management of phosphine fumigation may benefit from hermetic storage if the aforementioned factors are detected.”

Reference Correction

The reference “22. Mahlein, A.K. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. J. 2016, 100, 241–251. https://doi.org/10.1094/PDIS-03-15-0340-FE.”, was removed from the text of the article during the review process, but due to an error, it remained in the list of references. With this correction, the reference has been removed, and the order of some references has been adjusted accordingly.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.