Crop Distribution Research Articles

Desain Dan Analisa Performa Suspensi Hydraulicpneumatic Suspension System Kendaraan Pertanian Berbasis Mattrack

Indonesia is agrarian country whose people make their living as farmers, but until now still has hard to handle problem using agricultural’s technology, because the development of agricultural’s technology in Indonesia still inadequate, such as crop distribution, because some regions have texture of the ground surface is uneven, with use hydraulicpneumatic suspension and mattrack designed using SOLIDWORK is expected to be able to overcome the distribution of crop yields by adjusting Daihatsu Grandmax PU 1.3 STD. The main of this research is to see response of harmonic oscillatory sinusoidal with amplitude 0.02 m and frequency of 0.5 Hz, response of the damping force and spring force of a hydraulicpneumatic suspension using ideal PID controller. Simulation using MATLAB and Simulink to see continuous results with a time of 20 seconds. The test was carried out on the rear wheels with quarter vehicle systeml, by replacing the conventional suspension with a hydraulicpneumatic which has an orifice diameter of 2 mm and a piston of 78 mm using ASTM D 1298 oil of , for the gas accumulator it has the initial pressure and volume in both cylinders is , of with a floating piston diameter of 78 mm, produces harmonic oscillation movements of the vehicle that are smaller than the road profile conditions, and experience a stable condition at 6.2 seconds. And the maximum oscillating force produced by the mattrack is almost the same as the road profile conditions which produce good road holding.

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset.

Effects of soil physicochemical environment on the plasticity of root growth and land productivity in maize soybean relay strip intercropping system.

Soil is a key foundation of crop root growth. There are interactions between root system and soil in multiple ways. The present study aimed to further explore the response of root distribution and morphology to soil physical and chemical environment under maize (Zea mays L.) soybean (Glycine Max L. Merr.) relay strip intercropping (MS) An experiment was carried out aiming to examine the effects of nitrogen (N) applications and interspecific distances on root system and soil environment in MS. The two N application levels, referred to as no N application (NN) and conventional N application (CN), were paired with different interspecific distances: 30, 45 and 60 cm (MS30, MS45 and MS60) and 100 cm of monoculture maize and soybean (MM/SS100). The results demonstrated that MS45 increased the distribution of soil aggregates (> 2 mm) near the crop roots and maize soil nutrients status, which increased by 20.3% and 15.6%. Meanwhile, MS reduced soil bulk density, increased soil porosity and improved soil oxygen content. Optimization of the soil environment facilitated root growth. The MS45 achieved a better result on root distribution and morphology than the other configuration and also increased land productivity. Relay intercropped soybean with maize in interspecific row spacing of 45 cm, improved soil physicochemical environment, reshaped root architecture and optimized root spatial distribution of crops to achieve greater land productivity. © 2024 Society of Chemical Industry.

Sistem Informasi Geografis Pemetaan Distribusi Tanaman Jagung Terhadap Produktivitas Pertanian Di Kabupaten Rembang Tahun 2020

Abstract. Corn is the most important commodity crop after rice. Not only utilized as daily food, but there are many other uses. Rembang Regency is one of the corn producing areas in Central Java. However, the productivity of maize farming in this district is low. Many factors underlie why the productivity of maize crops is low. This study aims to examine the relationship between maize distribution and productivity. Mapping is done by utilizing Geographic Information Systems. Using Geographic Information Systems can help understand the relationship and cause and effect of the distribution of crops on the low productivity of corn in Rembang Regency.
 
 Keywords: Maize, Geographic Information System, Rembang Regency

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers.

К вопросу о перспективах возделывания овсяницы тростниковой (Festuca arundinacea Schreb.) на орошаемых землях Нижнего Поволжья

Purpose: to perform a review of an under-investigated bluegrass crop – tall fescue (Festuca arundinacea Schreb.), which is not widespread in forage production, but has some advantages over other bluegrass grasses. Discussion. Tall fescue is characterized by high seed and forage yields, a long-time growing season, early regrowth in spring, an ability to vegetate until late autumn, and an active regrow capacity. Fescue agrocenoses are responsive to irrigation and a high background of mineral nutrition. The ability to grow on saline and alkalini soils is also important. It is widely used for slope and ravines stabilization. Providing optimal combinations of plant life factors creates conditions for good growth, development and formation of high productivity for a long time as well as allows the crop to persist and prevail in multi-species agrocenoses in a wide variety of soil and climatic conditions – from forest and forest-steppe to steppe and semi-desert regions. Tall fescue grows well in damp places under natural conditions. This moisture-loving plant tolerates moisture-saturated, heavy loamy soils well. Regulation of the water regime of fescue crops ensures a more complete use of the biological potential of the crop, which is expressed in the receipt of two additional mowings, compared to natural moisture conditions. Tall fescue is used mainly in the form of hay, silage and haylage. Conclusion. Cultivation of tall fescue against the background of mineral fertilizer application and irrigation ensures an yield increase on average up to 15 t/ha of dry matter and up to 2.5 t/ha of crude protein collection. The wide distribution of this valuable crop in forage production in arid zones is hampered by the lack of scientifically based rational methods for creating highly productive grass stands and elements of cultivation technology.

Falcataria moluccana (Miq.) root distribution seedlings in response to nitrogen concentrations and tillage

Abstract Falcataria moluccana (Miq.) Barnaby & Grimes is an important species for forest plantation programmes in Malaysia and is widely used in the wood industry. However, its root interactions have not been widely investigated due to the limited methodologies and information about the root distribution of trees and crops in forest plantation and agroforestry systems. This study was conducted to determine the rhizotron-scale root interactions of F. moluccana at different tillage and nitrogen concentrations under four different treatments: control, tillage, fertiliser, and tillage with fertiliser. The rhizotron-scale experiment was conducted at a greenhouse where F. moluccana (Batai) seedlings were transplanted in transparent rhizotron tubes (onemetre-high transparent polycarbonate solid sheet) using topsoil and river sand to simulate natural growing conditions. Root Intensity (RI), Root Length Density (RLD), Specific Root Length (SRL), dried shoot bio-mass and root biomass were recorded. Root biomass and SRL were notably higher (25–50 cm depth) at 6 WAT (Weeks After Transplanting), and the shoot biomass (tillage + fertiliser) was significantly higher at 14 WAT. However, plants treated with different tillage and nitrogen concentrations showed no significant impact on the RI and RLD. Fertiliser treatment only, and tillage with fertiliser treatment, showed greater root distribution at the rhizotron scale. These findings contribute to forest plantation and natural forest rehabilitation efforts by helping optimise the soil resources within ecosystems for sustainable forest management.

Food Estate in Transmigrant Society, Central Borneo: Blessed or Curse?

The food estate program implemented in Central Borneo has elicited both positive and negative responses from diverse segments of society. The present scenario is closely intertwined with the pressing nature and consequential effects, particularly within the domains of environmental and ecosystem sciences, economics, and socio-cultural sciences. This research examines the implementation and effects of the food estate program on the transmigrant community in the Pandih Batu District, Pulang Pisau Regency, located in Central Borneo. It uses a qualitative research design. Data were gathered through observations, documentation, and interviews. The focus of this study pertains to the transmigrant community residing in the regions of Blanti, Siam, and Gadabung. Establishing a food estate in Pulang Pisau has yielded favorable outcomes within the community, including the provision of assistance and enhancements in infrastructure, particularly in terms of road accessibility for the distribution of crops. However, it also raises issues like ecosystem alterations and communal farming practices. Despite the need for the land to support the target’s achievement, people feel driven to boost the production of the agricultural products the government has set as a goal. The establishment of the food estate has resulted in a situation where farmers have become reliant on the government for the allocation of resources.

Including land management in a European carbon model with lateral transfer to the oceans

The use of cover crops (CCs) is a promising cropland management practice with multiple benefits, notably in reducing soil erosion and increasing soil organic carbon (SOC) storage. However, the current ability to represent these factors in land surface models remains limited to small scales or simplified and lumped approaches due to the lack of a sediment-carbon erosion displacement scheme. This precludes a thorough understanding of the consequences of introducing a CC into agricultural systems. In this work, this problem was addressed in two steps with the spatially distributed CE-DYNAM model. First, the historical effect of soil erosion, transport, and deposition on the soil carbon budget at a continental scale in Europe was characterized since the early industrial era, using reconstructed climate and land use forcings. Then, the impact of two distinct policy-oriented scenarios for the introduction of CCs were evaluated, covering the European cropping systems where surface erosion rates or nitrate susceptibility are critical. The evaluation focused on the increase in SOC storage and the export of particulate organic carbon (POC) to the oceans, compiling a continental-scale carbon budget. The results indicated that Europe exported 1.95 TgC/year of POC to the oceans in the last decade, and that CCs can contribute to reducing this amount while increasing SOC storage. Compared to the simulation without CCs, the additional rate of SOC storage induced by CCs peaked after 10 years of their adoption, followed by a decrease, and the cumulative POC export reduction stabilized after around 13 years. The findings indicate that the impacts of CCs on SOC and reduced POC export are persistent regardless of their spatial allocation adopted in the scenarios. Together, the results highlight the importance of taking the temporal aspect of CC adoption into account and indicate that CCs alone are not sufficient to meet the targets of the 4‰ initiative. Despite some known model limitations, which include the lack of feedback of erosion on the net primary productivity and the representation of carbon fluxes with an emulator, the current work constitutes the first approach to successfully couple a distributed routing scheme of eroded carbon to a land carbon model emulator at a reasonably high resolution and continental scale. Short abstractA spatially distributed model coupling erosion, transport, and deposition to the carbon cycle was developed. Then, it was used to simulate the impact of cover crops on both erosion and carbon, to show that cover crops can simultaneously increase organic carbon storage and reduce particulate organic carbon export to the oceans. The results seemed persistent regardless of the spatial distribution of cover crops.

RICE FIELDS MANAGEMENT AGREEMENT SYSTEM IN ACEH BESAR (Study of Engagement Patterns and Sharing of Harvest Results)

Rice field management, which is carried out by involving the people of Aceh Besar based on the norms of Islamic law and customary law, is applied from the beginning of the determination of the engagement to the capital of the harvest. Community practice describes the procedure for contracting and distributing crops in various forms. However, all farmers do it by mutual consent. This phenomenon is the focus of study in this paper. By basing on the method of discriptive analysis, the answers will be found, including: 1) the formation of contracts is carried out orally. The form of an oral contract is based on several things, namely the relationship between the parties; In general, the parties come from relatives or close family. Because of this relationship, they believe there will be no default made by the parties. In addition, there are two ways to start involvement in rice field management; First, half of the respondents agreed that capital is borne by rice field owners (this model is called muzara'ah), and half said capital is borne by farmers (this model is called mukhabarah). 2) the distribution of crops is carried out in the following way; 1) Mawah, which is divided equally between rice field owners and farmers. There are 3 people (20%); 2) Bulung 3, which is 2 parts for farmers, 1 part for rice field owners = 5 people (33.3%); 3) Bulung 4, which is 3 parts for farmers, 1 part for rice field owners = 4 people (26.6%); 4) Bulung 5, which is 3 parts for farmers, 2 parts for rice field owners =3 (20.1%). In addition to the three things already mentioned, the parties also agreed on the conditions of crop failure. Most engagements exempt farmers from giving rice to rice owners as a cost of using rice fields, while others require farmers to give rice to rice owners as rice field use fees.

Analysis and mitigation of illumination influences on canopy close-range hyperspectral imaging for the in situ detection of chlorophyll distribution of basil crops

Timely detection of chlorophyll distribution within crop canopies is crucial due to its non-uniform and dynamic characteristic. Close-range hyperspectral imaging (HSI) shows great potential for rapid quantifying the chlorophyll distribution on detached leaves, but the canopy HSI presents greater challenges due to the complex interaction of illumination with canopy geometry. The spectral pre-processing techniques have shown effectiveness in mitigating some negative effects; however, there has been lacking the comprehensive analysis and effective mitigation of illumination influences on close-range HSI of relatively complex canopies. In this study, the basil crops cultivated under different light intensities were taken as the object. Firstly, the chlorophyll, growth, and spectral responses of basil crops to varying light intensities were examined. Then, through a comprehensive analysis of the illumination influences on canopy close-range HSI, several spectral pre-processing methods were selected. Subsequently, by evaluating regression modeling performances and canopy chlorophyll distribution maps, the combination of Savitzky-Golay-standard normal variate (SG-SNV) spectral pre-processing method and random forest (RF) model was identified as the optimal close-range HSI analysis pipeline, which enabled the effective mitigation of illumination influences. Its application to hyperspectral images of basil canopies cultivated under different light intensities yielded satisfactory chlorophyll distribution maps, consistent with observed differences in chlorophyll levels. Furthermore, the reason behind the proposed analysis pipeline in mitigating illumination influences in complex canopies was discussed, that the spectral pre-processing method and appropriate nonlinear model were effective in reducing the linear (eg. working distance, surface angle) and nonlinear (eg. multiple scattering) effects, respectively. The overall results demonstrated that, based on the analysis of illumination influences, the proposed close-range HSI pipeline permitted the mitigation of these influences substantially, enabling the in situ detection of canopy chlorophyll distribution. This methodology holds significant importance for timely monitoring chlorophyll status of whole canopies, thereby enhancing the planting management strategies.

MaxEnt model strategies to studying current and future potential land suitability dynamics of wheat, soybean and rice cultivation under climatic change scenarios in East Asia.

Climate change and variability are projected to alter the geographic suitability of lands for crops cultivation. Accurately predicting changes in the potential current and future land suitability distribution dynamics of wheat (Triticum aestivum), soybean (Glycine max) and rice (Oryza sativa) crops due to climate change scenarios is critical to adapting and mitigating the impacts of bioclimatic changes, and plays a significant role in securing food security in East Asia region. This study compiled large datasets of wheat, soybean and rice occurrence locations from GBIF and 19 bioclimatic variables obtained from the WorldClim database that affect crops growth. We recognized potential future suitable distribution regions for crops under the one socioeconomic pathway, (SSP585) for 2021-2040 and 2041-2060, using the MaxEnt model. The accuracy of the MaxEnt was highly significant with mean AUC values ranging from 0.833 to 0.882 for all models evaluated. The jackknife test revealed that for wheat, Bio4 and Bio12 contributed 17.6% and 12.6%, for soybean Bio10 and Bio12 contributed 15.6% and 49.5%, while for rice Bio12 and Bio14 contributed 12.9% and 36.0% to the MaxEnt model. In addition, cultivation aptitude for wheat, soybean, and rice increased in southeast China, North Korea, South Korea, and Japan, while decreasing in Mongolia and northwest China. Climate change is expected to increase the high land suitability for wheat, soybean, and rice in East Asia. Simulation results indicate an average decrease of unsuitable areas of -98.5%, -41.2% and -36.3% for wheat, soybean and rice from 2060 than that of current land suitability. In contrast, the high land suitable for wheat, soybean and rice cultivation is projected to increase by 75.1%, 68.5% and 81.9% from 2060 as compared with current. The findings of this study are of utmost importance in the East Asia region as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain crops distribution under future climates. Although the risks of wheat, soybean and rice cultivation may be significantly higher in the future because of high temperatures, heat waves, and droughts caused by climate change.

Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning

Limited accurate ground truth labels are the primary constraint for data-driven modeling analysis of large-scale crop mapping. Existing labeling methods largely rely on field surveys, visual interpretation, and historical ground information. These labor-intensive approaches are often limited by spatiotemporal heterogeneity of crop distribution and encounter the challenge of gathering extensive crop labels. The massive operating trajectories of agricultural machinery contain precise location information of the crop fields, providing a new source for accurate crop labels at a large spatial scale. This study develops a large-scale crop mapping workflow through widespread harvester trajectory and 10 m Sentinel-2 imagery. The trajectory-based automatic labeling method is developed to generate 287,533 winter wheat labels by jointly using harvester coordinates and satellite images. These generated one-class ground labels are further used to develop positive and unlabeled learning based deep learning models for winter wheat mapping. The Positive and Unlabeled Learning-based Convolutional Neural Network (PUL-CNN) outperforms the other four one-class based classifiers with an F1 score of 94.4 % at 12 study sites. The estimated county-level winter wheat acreage agrees well with census data with R2 of 0.86 in the overall study region. The interpretation analysis based on the Shapley Additive Explanation method shows the heading and greening stages are the critical periods for wheat mapping, aligning well with the separability in Normalized Difference Vegetation Index (NDVI) curves. The results of winter wheat mapping demonstrate the integration of harvester trajectory and remote sensing data facilitates large-scale winter wheat mapping. To the best of our knowledge, this is the first study that fuses operating trajectories of agricultural machinery and satellite images for large-scale crop mapping based on the deep positive and unlabeled learning approach. This study could be possibly applied for better understanding the land cover and land use changes.

WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping

Abstract. The challenge of global food security in the face of population growth, conflict, and climate change requires a comprehensive understanding of cropped areas, irrigation practices, and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency-funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereal maps, seasonal irrigation maps, seasonal active cropland maps, and model confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. Validation of the products was done based on best available reference data per product. A global statistical validation for the temporary crop extent product resulted in user's and producer's accuracies of 88.5 % and 92.1 %, respectively. For crop type, a verification was performed against a newly collected street view dataset (overall agreement 82.5 %) and a limited number of publicly available in situ datasets (reaching minimum agreement of 80 %). Finally, global irrigated-area estimates were derived from available maps and statistical datasets, revealing the conservative nature of the WorldCereal irrigation product. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and to inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and to push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023).

Modelling Soil Water Infiltration and Wetting Patterns in Variable Working-Head Moistube Irrigation

Moistube irrigation is an efficient method that accurately irrigates and fertilizes agricultural crops. Investigation into the mechanisms of infiltration behaviors under an adjusted working head (WKH) benefits a timely and artificially regulating moisture condition within root zones, as adapted to evapotranspiration. This study explores the laws of Moistube irrigated soil water movement under constant and adjusted working heads. Lysimeter experiments were conducted to measure Moistube irrigation cumulative infiltration, infiltration rate, and to observe wetting front area and water content distribution using digital image processing and time domain reflectometry, respectively. Treatments of constant heads (0, 1, and 2 m), increasing heads (0 to 1, 0 to 2 and 1 to 2 m) and deceasing heads (1 to 0, 2 to 0 and 2 to 1 m) were designed. The results show that (1) under constant heads, the cumulative infiltration increases linearly over time. The infiltration rate and cumulative infiltration are positively correlated with the pressure head. When WKH is increased or decreased, the infiltration rate and cumulative infiltration curves significantly change, followed by a gradual stabilization. The more the head is increased or decreased, the more evident this tendency will be. (2) When WKH is increased, the wetting front migration rate and the wetted soil moisture content marked increase; when WKH is decreased, the wetting front migration rate sharply decelerates, and the water content of the wetted soil slowly grows. They both tend to equilibrium with time. (3) By regarding the same cumulative infiltration of increased WKH and constant WKH treatments as a similar initial condition, we proposed a cumulative infiltration empirical model for Moistube irrigation under variable working head. Additionally, we treat the Moistube as a clayey porous medium and construct a HYDRUS-2D numerical model to predict the infiltration behaviors under variable WKH. The validity of the two models were well proven, with MRE and NRMSE close to 0 and NSE greater than 0.867, indicating good agreements with the experimental results. This model breaks through the limitation of constant boundary of traditional numerical model and applies variable head boundary to the boundary of the Moistube pipe, which can also effectively simulate the response mechanism of Moistube irrigation to variable WKH. The research results further confirmed the feasibility of manually adjusting the WKH to regulate the discharge of the Moistube pipe and soil moisture state. Based on the HYDRUS-2D numerical model simulation results and the root distribution and water demand of typical facility crops, the selection range of placement depth and the adjustable range of WKH of Moistube irrigation were proposed. The research results provide a theoretical reference for manual adjustment or automatic control of Moistube irrigation WKH to adapt to real-time crop water demand in agricultural production.

Evaluating the potential of multi-temporal Sentinel-1 and Sentinel-2 data for regional mapping of olive trees

ABSTRACT Olives are a crucial economic crop in Mediterranean countries. Detailed spatial information on the distribution and condition of crops at regional and national scales is essential to ensure the continuity of crop quality and yield efficiency. However, most earlier studies on olive tree mapping focused mainly on small parcels using single-sensor, very high resolution (VHR) data, which is time-consuming, expensive and cannot feasibly be scaled up to a larger area. Therefore, we evaluated the performance of Sentinel-1 and Sentinel-2 data fusion for the regional mapping of olive trees for the first time, using the Izmir Province of Türkiye, an ancient olive-growing region, as a case study. Three different monthly composite images reflecting the different phenological stages of olive trees were selected to separate olive trees from other land cover types. Seven land-cover classes, including olives, were mapped separately using a random forest classifier for each year between 2017 and 2021. The results were assessed using the k-fold cross-validation method, and the final olive tree map of Izmir was produced by combining the olive tree distribution over two consecutive years. District-level areas covered by olive trees were calculated and validated using official statistics from the Turkish Statistical Institute (TUIK). The K-fold cross-validation accuracy varied from 94% to 95% between 2017 and 2021, and the final olive map achieved 98% overall accuracy with 93% producer accuracy for the olive class. The district-level olive area was strongly related to the TUIK statistics (R2 = 0.60, NRMSE = 0.64). This study used Sentinel data and Google Earth Engine (GEE) to produce a regional-scale olive distribution map that can be scaled up to the entire country and replicated elsewhere. This map can, therefore, be used as a foundation for other scientific studies on olive trees, particularly for the development of effective management practices.

Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery

Accurate crop mapping is of great significance for crop yield forecasting, agricultural productivity development and agricultural management. Thanks to its all-time and all-weather capability, integrating multi-temporal synthetic aperture radar (SAR) for crop mapping has become essential and challenging task in remote sensing. In recent years, deep learning (DL) has demonstrated excellent crop mapping accuracy to interpret crop dynamics. However, existing DL-based methods tend to be incapable of capturing spatial and temporal features at different scales simultaneously, and this often leads to severe mis-classification due to the complex and heterogeneous distribution of crops and diverse phenological patterns. In this paper, we propose a novel spatio-temporal multi-level attention method, named as STMA, for crop mapping using time-series SAR imagery in an end-to-end fashion to increase the capability of crop phenology retrieval. Specifically, the multi-level attention mechanism is designed to aggregate multi-scale spatio-temporal representations on crops via cascaded spatio-temporal self-attention (STSA) and multi-scale cross-attention (MCA) modalities. To ensure a fine extraction of multi-granularity features, a learnable spatial attention position encoding is proposed to adaptively generate the position priors to facilitate multi-level attention learning. Experimental results on Brandenburg Sentinel-1 dataset, public PASTIS-R dataset and South Africa dataset demonstrated that STMA can achieve state-of-the-art performance in crop mapping tasks, with the accuracy of 96.54% in the Brandenburg Sentinel-1 dataset, 86.77% in the PASTIS-R dataset and 83.37% in the South Africa dataset, validating its effectiveness and superiority. Further comparison of spatio-temporal generalization capability reflected its excellent performance in spatio-temporal modeling on different crops and scenarios. This research provides a viable and intelligent spatio-temporal framework for large-area crop mapping using time-series SAR imagery in complex agricultural systems. The Brandenburg Sentinel-1 dataset and the STMA code will be publicly available at https://github.com/hanzhu97702/ISPRS_STMA.

Study on the Crop Suitability and Planting Structure Optimization in Typical Grain Production Areas under the Influence of Human Activities and Climate Change: A Case Study of the Naoli River Basin in Northeast China

Optimizing crop planting structures under the influence of climate change and human activities is crucial for sustainable food production and global food security. Taking the Naoli River Basin in Northeast China as a case area, a machine learning model based on maximum entropy was used to explore the suitability distribution of crops under the influence of both environmental factors and human activities. The optimized planting structure strategies were tested in combination with future climate change. The results show that considering human activities can more accurately simulate crop suitability than considering only natural environmental factors. The suitable planting areas for maize, rice, and soybeans are 18,553.54 km2, 10,335.98 km2, and 5844.80 km2, respectively. Highly adapted areas for major crops are concentrated in the plain areas of the middle reaches of the river basin, rather than in populated areas, and there are overlaps among the suitable planting areas for each crop. The optimal crop distribution for the planting structure is to plant rice in the hydrophilic areas of the plain hinterland, soybeans in the plain hinterland farther from the water source, and corn in the peripheral plains and gently sloping mountainous areas. Human activities exerted a strong influence on the potential scatter of soybeans, while climate change had the most significant implications for maize. Future climate change may reduce the area of suitable crop zones, posing challenges to regional food production. It is necessary to reflect on how to rationally balance soil and water resources, as well as how to cope with climate change in the future.

Advances and opportunities in unraveling cold-tolerance mechanisms in the world's primary staple food crops.

Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end-use quality, and distribution of key staple crops including rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold-responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low-temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold-tolerant crops for the future.

Crop Yield Prediction using Machine Learning and Deep Learning Techniques

Crop yield prediction has been designated as a major predictive analysis technique that increases the potential of the agricultural industry. The utilisation of such a measure has been important for the farmers to understand the yields of crops during the particular season from a data analytical point of view. Such an aspect has fallen under the concept of predictive analysis which allows the farmers, agricultures, and farming businessmen to make strategic decisions in terms of cultivation. The application of predictive analysis has been useful for understanding the specific set of crops to be sown during the season, and the types of fertilisers to be applied to the crops for an increased output. Risk analysis with the help of predictive modelling of crops helps in the improvement of the overall agriculture business and increases the potential of the farmers to improve their revenue collection. Once they have the potential of understanding the specific parameters of agriculture, the decision making for reducing the risks, increasing the overall gain from the crops, and such other aspects can be easily known. Predictive analysis allows the farmers to gain an expansive amount of knowledge regarding the weather conditions in the future, the quality of the soil for growing the crops, the nutrients required which are to be replenished for increasing the crop field, and several such parameters. Machine Learning or ML and Deep Learning or DL methods have been seen to be extremely important for data analysis and predictions. Several kinds of tools and techniques such as neural networking, Bi GRU, Maxout classifiers, and others have been applied within the agricultural industry. The study would lead to an extensive analysis of the different kinds of Machine Learning and Deep Learning techniques used for increasing the crop yields by prediction analysis. Such a measure would prove to be extremely important to make significant decisions regarding the importing and exporting of crops, and the pricing structure for the grains to be sold in the market. The distribution of crops and also making fruitful decisions regarding future crop plantations can also be inspected with the help of the ML and DL tools.