The Influence of Rootstock on Fruit Ethylene, Respiration, Index of Absorbance Difference, Fruit Quality, and Production of ‘Aztec Fuji’ Apple under a Full-crop Condition

Selection of dwarfing rootstocks that facilitate optimum production of high-quality fruit is crucial in modern high-density apple orchards. In addition to tree growth and yield, rootstocks can influence fruit maturity of scion cultivars in apples. In this study, the impact of 17 rootstocks on fruit maturity, yield, and quality attributes of ‘Aztec Fuji’ apples (Malus domestica Borkh.) at harvest were evaluated in a season when all trees were in a “full-crop” condition. Keeping sealed fruit at room temperature, a typical climacteric pattern was observed in ethylene evolution, respiration, and oxygen consumption, peaking after 5–7 days in fruit from trees on all rootstocks. During the ripening period, ethylene evolution and respiration rates in fruit from trees on Supp.3, G.3001, and G.202 were often in the high-range category, whereas those on CG.4004, CG.4214, G.41N, and B.9 were in the midrange category and those on M.9Pajam2, M.26EMLA, and G.11 were in the low-range category. Evolved ethylene and respiration in fruit from trees on M9.T337 steadily and slowly increased from 7 days after harvest (7DAH) to 13 days after which harvest (13DAH) ethylene sharply increased, signaling occurrence of climacteric peak, while respiration declined after the peak of 13DAH. In fruit from trees on most rootstocks, the rates of oxygen consumption had inverse relationships with the rates of respiration, so that fruit from trees on M9.T337 had higher and those on G.41N and Supp.3 had lower rates of oxygen consumption. Trees on G.41N, CG.4004, and M.26EMLA had higher and those on CG.4003 had lower yield per tree than trees on other rootstocks. Trees on B.9 and M.9T337 were most yield efficient among trees on all rootstocks. Trees on CG.4004 had larger fruits than those on other rootstocks. Considering all fruit maturity, quality, and yield attributes, CG.4004 seems to be a good choice of rootstock for ‘Aztec Fuji’ under the conditions of this study.

Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery

An effective crop management practice is very important to the sustenance of crop production. With the emergence of Industrial Revolution 4.0 (IR 4.0), precision farming has become the key element in modern agriculture to help farmers in maintaining the sustainability of crop production. Unmanned aerial vehicle (UAV) also known as drone was widely used in agriculture as one of the potential technologies to collect the data and monitor the crop condition. Managing and monitoring the paddy field especially at the bigger scale is one of the biggest challenges for farmers. Traditionally, the paddy field and crop condition are only monitored and observed manually by the farmers which may sometimes lead to inaccurate observation of the plot due the large area. Therefore, this study proposes the application of unmanned aerial vehicles and RGB imagery for monitoring rice crop development and paddy field condition. The integration of UAV with RGB digital camera were used to collect the data in the paddy field. Result shows that the early monitoring of rice crops is important to identify the crop condition. Therefore, with the use of aerial imagery analysis from UAV, it can help to improve rice crop management and eventually is expected to increase rice crop production.

Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields

Agricultural field identification is still a difficult issue because of the poor resolution of satellite imagery. Monitoring remote harvest and determining the condition of farmlands rely on the digital approach agricultural applications. Therefore, high-resolution photographs have obtained much more attention since they are more efficient in detecting land cover components. In contrast, because of low-resolution repositories of past satellite images used for time series analysis, wavelet decomposition filter-based analysis, free availability, and economic concerns, low-resolution images are still essential. Using low-resolution Synthetic Aperture Radar (SAR) satellite photos, this study proposes a GAN strategy for locating agricultural regions and determining the crop's cultivation state, linked to the initial or harvesting time. An object detector is used in the preprocessing step of training, followed by a transformation technique for extracting feature information and then the GAN strategy for classifying the crop segmented picture. After testing, the suggested algorithm is applied to the database's SAR images, which are further processed and categorized based on the training results. Using this information, the density between the crops is calculated. After zooming in on SAR photos, the crop condition may be categorized based on crop density and crop distance. The Euclidean distance formula is used to calculate the distance. Finally, the findings are compared to other existing approaches to determine the proposed technique's performance using reliable measures.

An Overview of Precision Irrigation Systems Used in Agriculture

The introduction of precision agriculture increased the efficiency of plant production, while simultaneously reducing the production cost. Precision irrigation can be considered as the combination of sensors, computer software and irrigation systems. Precision irrigation has reduced water consumption and increased yields, and thus increased economic profits. The development of new crop monitoring technologies in precision irrigation has been made possible by the imaging and analysis of real-time crop condition data. The aim of this study was to describe the present state and possibilities of precision irrigation in practice in the EU and Croatia. An overview of the current precision irrigation technologies, as well as its adaptive management to the decision-making in agricultural water management, represents a fundamental basis for future practical studies in precision irrigation.

THE INFLUENCE OF DIFFERENT PURITY OF NATURAL ALFALFA SEEDS ON THE PROCESSING EFFICIENCY

This paper presents the results of the seed processing of ten lots of natural alfalfa seed with different purity (from 68.0% to 86.5%). The test was carried out at the seed processing center of the Institute for forage crops Kruševac-Serbia. Seed losses, processing output, seed yield and quality of the processed seed were investigated. It is important that the difference between the amounts of pure seed from laboratory assessment and the actual amount after processing, are low. The purity of natural alfalfa seed depends on the crop condition and the harvest process. In the seed processing of small-grained leguminous plants, the processing output of seed is directly dependent on the percentage of weed species and other species in the natural seed. Seeds of quarantine weeds of dodder and curly dock are a particularly big problem in alfalfa seeds. In the case of high-purity seeds with low quarantine weeds share, processing output are high. By the legal procedure on the seed quality, the content of pure seed, inert materials, weeds and other species in the processed seeds is defined. The efficiency of the alfalfa seed processing depends on the initial purity of the seed, as well as the applied technical and technological process of seed processing. Based on the obtained results, it is possible to optimally adjust and select the appropriate equipment for the processing of alfalfa seed, depending on the quantity and type of weeds and other ingredients in the natural alfalfa seeds.

Monitoring Crop Status in the Continental United States Using the SMAP Level-4 Carbon Product

Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4–0.7) and matured (r: 0.6–0.9) and that the agreement was better in drier regions (r: 0.4–0.9) than in wetter regions (r: −0.8–0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research.

UAV IMAGING AT RGB FOR CROP CONDITION MONITORING

Modern techniques such as precision agriculture tasks are provided to intentional fertilization, pesticide dosing or simply watering the crops. These tasks need to be continuously monitored. One of known method for analyzing the crops conditions is calculating the vegetation indexes. This paper focuses on purpose of using images made with UAV equipped with ordinary non-metric digital RGB camera. The methods had been taken revealed easy to use and cost effective. We present an experiment which attend to distinguish different crops conditions on two test fields sowed with wheat and rape. For this purpose, two different RGB based vegetation indexes were analyzed. The results of calculated indexes shown how crops differs in each stage of vegetation. During the first stage (germinating) the plants are green and average TGI is low. It increases at second stage (flowering) because of plant flowers, which partly cover the leaves. At last stage (ripening) TGI decreases, so plants are still green but starting to dry and change their color.

AUTOMATED CLASSIFICATION OF CROP TYPES AND CONDITION IN A MEDITERRANEAN AREA USING A FINE-TUNED CONVOLUTIONAL NEURAL NETWORK

Crop classification based on satellite and aerial imagery is a recurrent application in remote sensing. It has been used as input for creating and updating agricultural inventories, yield prediction and land management. In the context of the Common Agricultural Policy (CAP), farmers get subsidies based on the crop area cultivated. The correspondence between the declared and the actual crop needs to be monitored every year, and the parcels must be properly maintained, without signs of abandonment. In this work, Sentinel- 2 time series images and 4-band Very High Resolution (VHR) aerial orthoimages from the Spanish National Programme of Aerial Orthophotography (PNOA) were combined in a pre-trained Convolutional Neural Network (CNN) (VGG-19) adapted with a double goal: (i) the classification of agricultural parcels in different crop types; and (ii) the identification of crop condition (i.e., abandoned vs. non-abandoned) of permanent crops in a Mediterranean area of Spain. A total of 1237 crop parcels from the CAP declarations of 2019 were used as ground truth to classify into cereals, fruit trees, olive trees, vineyards, grasslands and arable land, from which 80% were used for training and 20% for testing. The overall accuracy obtained was greater than 93% both, at parcel and area levels. Olive trees were the least accurate crop, mostly misclassified with fruit trees, and young vineyards were slightly confused with cereal and arable land. In the assessment of crop condition, only 9.65% of the abandoned plots were missed (omission errors), and 7.21% of plots were over-detected (commission errors), having a 99% of overall accuracy from a total of 1931 image subset samples. The proposed methodology based on CNN is promising for its operational application in crop monitoring and in the detection of abandonments in the context of CAP subsidies, but a more exhaustive number of training samples is needed for extension to other crop types and geographical areas.

Qualitative crop condition survey reveals spatiotemporal production patterns and allows early yield prediction

Large-scale continuous crop monitoring systems (CMS) are key to detect and manage agricultural production anomalies. Current CMS exploit meteorological and crop growth models, and satellite imagery, but have underutilized legacy sources of information such as operational crop expert surveys with long and uninterrupted records. We argue that crop expert assessments, despite their subjective and categorical nature, capture the complexities of assessing the “status” of a crop better than any model or remote sensing retrieval. This is because crop rating data naturally encapsulates the broad expert knowledge of many individual surveyors spread throughout the country, constituting a sophisticated network of “people as sensors” that provide consistent and accurate information on crop progress. We analyze data from the US Department of Agriculture (USDA) Crop Progress and Condition (CPC) survey between 1987 and 2019 for four major crops across the US, and show how to transform the original qualitative data into a continuous, probabilistic variable better suited to quantitative analysis. Although the CPC reflects the subjective perception of many surveyors at different locations, the underlying models that describe the reported crop status are statistically robust and maintain similar characteristics across different crops, exhibit long-term stability, and have nation-wide validity. We discuss the origin and interpretation of existing spatial and temporal biases in the survey data. Finally, we propose a quantitative Crop Condition Index based on the CPC survey and demonstrate how this index can be used to monitor crop status and provide earlier and more precise predictions of crop yields than official USDA forecasts released midseason.