Crop Field Management Research Articles

A Hybrid Synthetic Minority Oversampling Technique and Deep Neural Network Framework for Improving Rice Yield Estimation in an Open Environment

Quick and accurate prediction of crop yields is beneficial for guiding crop field management and genetic breeding. This paper utilizes the fast and non-destructive advantages of an unmanned aerial vehicle equipped with a multispectral camera to acquire spatial characteristics of rice and conducts research on yield estimation in an open environment. The study proposes a yield estimation framework that hybrids synthetic minority oversampling technique (SMOTE) and deep neural network (DNN). Firstly, the framework used the Pearson correlation coefficient to select 10 key vegetation indices and determine the optimal feature combination. Secondly, it created a dataset for data augmentation through SMOTE, addressing the challenge of long data collection cycles and small sample sizes caused by long growth cycles. Then, based on this dataset, a yield estimation model was trained using DNN and compared with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). The experimental results indicate that the hybrid framework proposed in this study performs the best (R2 = 0.810, RMSE = 0.69 t/ha), significantly improving the accuracy of yield estimation compared to other methods, with an R2 improvement of at least 0.191. It demonstrates that the framework proposed in this study can be used for rice yield estimation. Additionally, it provides a new approach for future yield estimation with small sample sizes for other crops or for predicting numerical crop indicators.

Research on Estimating Potato Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop’s entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.

Maize emergence rate and leaf emergence speed estimation via image detection under field rail-based phenotyping platform

Accurate and efficient acquisition of maize emergence rate and leaf emergence speed in the field is essential for detecting seed quality, evaluating crop field management plans, and yield assessment. This study constructs a system solution to obtain the maize seedling emergence rate and leaf emergence speed based on the field rail-based phenotyping platform and convolutional neural network. Firstly, we use the field rail-based phenotyping platform to collect a high-temporal sequence visible light images of maize plant during the seedling stage. In the first stage, an improved Faster R-CNN is used to detect maize seedlings in the plot images, and the plant ROI area is cropped as the input for the second stage network. In the second stage, the best performing ResNeSt network out of four backbone networks is chosen, using the Mask R-CNN model to segment the leaves of the input plant image, which is then used to calculate the number of leaves. We propose a quantification index for leaf emergence speed based on a weighted average combination of leaf numbers. Using the method described in this paper, we analyzed the plant images from 52 inbred lines plots of over seven consecutive days. The experimental results show that when the Intersection Over Union (IOU) is 0.50, the bbox_mAP of the maize seedling detection model is 0.969, with an accuracy rate of 99.53%. Compared with manual counting, the calculated R2 is 0.997 and RMSE is 43.382. The segm_mAP of the plant leaf segmentation model is 0.942. The differences in emergence rate and leaf emergence speed across 52 inbred lines were compared, providing new phenotyping reference indices for further exploring the genotypic differences affecting seed emergence and leafing.

WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network

BackgroundDetection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications.ResultsIn our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61).ConclusionsExtensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.

Role Of Integrated Crop Management Field Schools In Increasing Rice Production Results

Field School - Integrated Crop Management (SL-PTT) is a form of school where the entire teaching and learning process is carried out in the field, which is carried out on the land of PTT participating farmers in an effort to increase national rice production. This SL-PTT guide is the result of discussions with the Directorate General of Food Crops and the Agricultural HR Development Agency. So it is very interesting to examine (a) the role of integrated crop management field schools in increasing rice production. Respondents for this study were selected purposively, namely 1 extension worker and 4 heads of farmer groups. The research results show 1. The influence of adult education methods (andragogy) on farmers in SLPTT activities, namely the learning process in SL-PTT starts with activities which then provide personal experiences, express these experiences, analyze problems that occur, and conclude the results of activities. Every learner is encouraged to be able to observe reality, express experiences and ideas, analyze facts, make decisions, and carry out activities together. The role of SLPTT is to increase knowledge and skills in recognizing potential, preparing farming plans, overcoming problems, making decisions, and applying technology that is appropriate to local resource conditions in a synergistic and environmentally sound manner, so that farming becomes efficient, highly productive and sustainable.ACT
 Keywords: Role, Integrated Crop Management Field School, Rice Production Results

PEMBERDAYAAN PETANI MELALUI PROGRAM SEKOLAH LAPANG PENGELOLAAN TANAMAN TERPADU PADI SAWAH. Studi Pada Kelompok Tani Di Desa Linamnutu Kecamatan Amanuban Selatan Kabupaten Timor Tengah Selatan

This study aims to determine (1) the extent of the integrated crop management field school program in farmer groups through human development,capital development, institutional development. (2) know the level of success of the field school program integrated rice crop management to increase productivity and income of farmers in the farmer groups in the village linamnutu Amanuban southern sub-district , east middle south.
 The method used is the survey method. The location was determined deliberately ( purposive sampling ) namely linamnutu village, south amanuban district, south central timor regency. The population of this study is the farmer groups involved in the field school program for integrated crop management namely : Taubtit farmer group, Brother farmer group, one-year peasant group and 63 members. Farmer samples are determined by proportional random sampling with the slovin formula, so that a sample of 39 farmers is obtained. The analysis used in this research is descriptive qualitative and the average analysis uses a Likert scale approach.
 The results of the study note that 72% of respondents belong to productive adulthood and 28% of non-productive age (>55) years. Formal education is classified as low (38%) of the total respondents ie educated to graduate from elementary scholl ), non-formal education (100%), and farm area is at intervals >0,5 Ha, Which is 74%. The field school integrated crop management field program has succeeded in applying the technology component of lowland rice cultivation, and is in the “High” category with an average score of 100%. Farmer business development in Linamnutu village increased by applying the cultivation technology component and was in the “ High” category with an average of 90.29%. Farmer capital development in linamnutu village increases by applying the technology component of rice cultivation and is in the “High” category with an average score 98.86%. Institutions in the village of Linamnutu improved by applying the technology component if rice cultivation to be in the “ Dynamic “ category with an average score of 93.89%.

A wheat spike detection method based on Transformer.

Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs). However, traditional CNNs equip with the inductive bias of locality and scale-invariance, which makes it hard to extract global and long-range dependency. In this paper, we propose a Transformer-based network named Multi-Window Swin Transformer (MW-Swin Transformer). Technically, MW-Swin Transformer introduces the ability of feature pyramid network to extract multi-scale features and inherits the characteristic of Swin Transformer that performs self-attention mechanism by window strategy. Moreover, bounding box regression is a crucial step in detection. We propose a Wheat Intersection over Union loss by incorporating the Euclidean distance, area overlapping, and aspect ratio, thereby leading to better detection accuracy. We merge the proposed network and regression loss into a popular detection architecture,fully convolutional one-stage object detection, and name the unified model WheatFormer. Finally, we construct a wheat spike detection dataset (WSD-2022) to evaluate the performance of the proposed methods. The experimental results show that the proposed network outperforms those state-of-the-art algorithms with 0.459 mAP (mean average precision) and 0.918 AP50. It has been proved that our Transformer-based method is effective to handle wheat spike detection under complex field conditions.

Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index

Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

Improved YOLO v5 Wheat Ear Detection Algorithm Based on Attention Mechanism

The detection and counting of wheat ears are essential for crop field management, but the adhesion and obscuration of wheat ears limit detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. Previous research results have shown that most methods for detecting wheat ears are of two types: colour and texture extracted by machine learning methods or convolutional neural networks. Therefore, we proposed an improved YOLO v5 algorithm based on a shallow feature layer. There are two main core ideas: (1) to increase the perceptual field by adding quadruple down-sampling in the feature pyramid to improve the detection of small targets, and (2) introducing the CBAM attention mechanism into the neural network to solve the problem of gradient disappearance during training. CBAM is a model that includes both spatial and channel attention, and by adding this module, the feature extraction capability of the network can be improved. Finally, to make the model have better generalization ability, we proposed the Mosaic-8 data enhancement method, with adjusted loss function and modified regression formula for the target frame. The experimental results show that the improved algorithm has an mAP of 94.3%, an accuracy of 88.5%, and a recall of 98.1%. Compared with the relevant model, the improvement effect is noticeable. It shows that the model can effectively overcome the noise of the field environment to meet the practical requirements of wheat ear detection and counting.

Quick and accurate monitoring peanut seedlings emergence rate through UAV video and deep learning

During the seedling stage, real-time monitoring and detection of seed germination are important for testing the quality of seeds, crop field management, and yield estimation. However, owing to the low efficiency of traditional manual seedling rate monitoring, survey methods have been gradually replaced by unmanned aerial vehicles (UAVs) and real-time peanut video counting models. In this study, we propose an efficient and fast real-time peanut video counting model (combining the improved YOLOV5s, DeepSort, and OpenCV programs) to accurately distinguish peanut seedlings from weeds, and to count peanut seedlings based on videos. The improved YOLOV5s combines a vision transformer with CSNet to replace the original CSNet backbone. The field experiment results show that the real-time peanut video counting model count capabilities is close to those of humans with an accuracy of 98.08%; however, the seedling calculation model takes only one-fifth of the time required for human detection. Therefore, the video-based model outperforms the image-based target detection algorithm, and was more suitable for application in practical germination rate investigation in peanut production.

Design and experiment of the components for soil flow direction control of hilling machine based on EDEM

Cultivation and hilling are important steps in crop field management and provide an important guarantee of crop quality and quantity. With the aim of addressing how the soil flow direction of a traditional cultivation and hilling machine is difficult to control, and because it is difficult to achieve high ridge soil cultivation, among other issues, the components of the soil flow direction control of a hilling machine was designed. The components of the soil flow direction control consisted of a soil-feeding plough device, spiral knives and guide cover devices, etc. The design and analysis of the guiding parts for the two tools of the soil-feeding plough device and the spiral knife were performed to obtain an appropriate guide wall and helix angle. The guiding principle in the flow direction control components was analyzed. The design of the guide wall adopts the torsional columnar plough surface, and the elementary line angle changes from stable to increasing. The analysis of spiral milling showed that when the spiral angle is large, the milling effect is better. According to the discrete element method, the working part of the machine-soil interaction model was established. EDEM software was used to simulate the control components for the soil flow direction of the hilling machine for compound cutting. The design method with a two-factor comprehensive test was used to study the linear velocity along the outer part of the spiral knife and the influence of the forward velocity of machine of the implement on the soil cultivation effect. The results of the discrete element simulation showed that both the linear velocity along the outer part of the spiral knife and the forward velocity of the machine have extremely significant effects on the transportation of particles to the ridge top and the hilling thickness. Following multiple comparisons of the average hilling thickness with different linear velocities along with the outer spiral knife and different forward velocities of the machine, it is concluded that the performance of the machine is better when the linear velocity along the outer spiral knife is 3.01 m/s and the forward velocity of the machine is 0.7 m/s. The research conclusion had great theoretical value and practical significance for the design of the machine, which worked for cultivation and hilling. Keywords: compound cutting, hilling machine, flow direction control, discrete element method, design DOI: 10.25165/j.ijabe.20221503.5857 Citation: Tong Z W, Li L H, Zhang X L, Chen Y, Liu X C, Zhou P L, et al. Design and experiment of the components for soil flow direction control of hilling machine based on EDEM. Int J Agric & Biol Eng, 2022; 15(3): 122–131.

Pengaruh Intensitas Komunikasi Penyuluh Terhadap Adopsi Petani dalam Program Sekolah Lapang Pengelolaan Tanaman Terpadu (SLPTT) Padi Sawah di Kecamatan Wawotobi Kabupaten Konawe

This study aimed to determine the effect of the intensity of the extension worker's communication on the adoption of farmers in the field school program for integrated crop management (SLPTT) for lowland rice in Wawotobi sub-district, Konawe district. The sampling technique used was purposive random sampling, and the data analysis used was descriptive and inferential statistics with simple linear regression. The research method used is a quantitative analysis method using primary data such as observations and questionnaires. The results of this studied indicate that the intensity of the extension worker's communication on farmer adoption in the field school program for integrated crop management (SLPTT) for lowland rice showed a positive and significant influence and contributes 32 ,8%. Regression equation X is obtained, which is = 37.502 = 0.367X, which means that the better the Intensity of Intensity of Communication for the Integrated Crop Management Field School Program (SLPTT), the higher the adoption of farmers in the program (SLPTT).

Rapid Detection and Counting of Wheat Ears in the Field Using YOLOv4 with Attention Module

The detection and counting of wheat ears are very important for crop field management, yield estimation, and phenotypic analysis. Previous studies have shown that most methods for detecting wheat ears were based on shallow features such as color and texture extracted by machine learning methods, which have obtained good results. However, due to the lack of robustness of these features, it was difficult for the above-mentioned methods to meet the detection and counting of wheat ears in natural scenes. Other studies have shown that convolutional neural network (CNN) methods could be used to achieve wheat ear detection and counting. However, the adhesion and occlusion of wheat ears limit the accuracy of detection. Therefore, to improve the accuracy of wheat ear detection and counting in the field, an improved YOLOv4 (you only look once v4) with CBAM (convolutional block attention module) including spatial and channel attention model was proposed that could enhance the feature extraction capabilities of the network by adding receptive field modules. In addition, to improve the generalization ability of the model, not only local wheat data (WD), but also two public data sets (WEDD and GWHDD) were used to construct the training set, the validation set, and the test set. The results showed that the model could effectively overcome the noise in the field environment and realize accurate detection and counting of wheat ears with different density distributions. The average accuracy of wheat ear detection was 94%, 96.04%, and 93.11%. Moreover, the wheat ears were counted on 60 wheat images. The results showed that R2 = 0.8968 for WD, 0.955 for WEDD, and 0.9884 for GWHDD. In short, the CBAM-YOLOv4 model could meet the actual requirements of wheat ear detection and counting, which provided technical support for other high-throughput parameters of the extraction of crops.

Integrated pest and pollinator management – expanding the concept

The objective of integrated pest and pollinator management (IPPM) is to co‐manage for pest control and pollination goals. Departing from the well‐established concept of integrated pest management, we include pollinator management in a hierarchical decision support system of management actions. We depict this support system as an IPPM pyramid. Priority is given to proactive measures at the base of the pyramid, which are undertaken through landscape and crop field management of mobile organisms, primarily arthropods. Farther up the pyramid, practices in the form of reactive use of biotic and abiotic inputs should align with basal actions. The goal of IPPM is to minimize trade‐offs, and to maximize co‐benefits and synergies between pest and pollinator management. We contend that IPPM has the potential to contribute to sustainable pest control and crop pollination, as well as provide broader environmental benefits.

Development of novel technological approaches for a reliable crop characterization under changing environmental conditions

The expansion of world population requires the development of new strategies and tools for agriculture. Extensive breeding and agronomic efforts over the past 50 years have been responsible for tripling cereal yields, while advances in grain quality have been less evident. Continuing advances in the techniques available to breeders offer the potential to increase the rate of genetic improvement aiming to develop resilient crop and better (more resource use efficient) varieties. Plant breeders want to be able to phenotype large numbers of lines rapidly and accurately identify the best progeny. For this purpose, different methodological approaches have been proposed to evaluate these traits in the field: (1) proximal (remote) sensing and imaging, (2) laboratory analyses of samples, and (3) lab-based near-infrared reflectance spectroscopy analysis in the harvestable part of the crop. However, near-infrared reflectance spectroscopy-based field evaluation of yield and grain quality is currently a real option. Thus the development of new technological approaches, such as the use of hyperspectral imaging sensors or near-infrared reflectance spectroscopy under field conditions may be critical as a phenotypic approach for efficient breeding as well as in field management of crops. This article reports the description of the CropYQualT-CEC project funded by the H2020-MSCA-RISE program. This project pursues the main objective of generating a common solid knowledge basis within the context of precision agriculture and digital farming. Further, within the project context, the article also provides a case study in which prediction models for total grain protein content, based on the reflectance spectrum of wheat canopies, are presented. Measurements were performed at around anthesis, using a full range near-infrared reflectance spectroscopy field spectrometer. Several models explaining >60% of grain protein variance in field trials illustrate the predictive capacity and robustness of this methodology for inferring grain quality traits well in advance of harvest.

Highly diverse root endophyte bacterial community is driven by growth substrate and is plant genotype-independent in common bean (Phaseolus vulgaris L.).

The common bean (Phaseolus vulgaris L.) is the most important grain legume in the human diet with an essential role in sustainable agriculture mostly based on the symbiotic relationship established between this legume and rhizobia, a group of bacteria capable of fixing atmospheric nitrogen in the roots nodules. Moreover, root-associated bacteria play an important role in crop growth, yield, and quality of crop products. This is particularly true for legume crops forming symbiotic relationships with rhizobia, for fixation of atmospheric N2. The main objective of this work is to assess the substrate and genotype effect in the common bean (Phaseolus vulgaris L.) root bacterial community structure. To achieve this goal, we applied next-generation sequencing coupled with bacterial diversity analysis. The analysis of the bacterial community structures between common bean roots showed marked differences between substrate types regardless of the genotype. Also, we were able to find several phyla conforming to the bacterial community structure of the common bean roots, mainly composed by Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes. Therefore, we determined that the substrate type was the main factor that influenced the bacterial community structure of the common bean roots, regardless of the genotype, following a substrate-dependent pattern. These guide us to develop efficient and sustainable strategies for crop field management based on the soil characteristics and the bacterial community that it harbors.

Crop Field Management and Social Structure at Gricignano d’Aversa (Campanian Plain, Southern Italy) in the Early Bronze Age

ABSTRACT Campanian Plain (southern Italy) mid-late Holocene landscape information is preserved by repeated eruption debris from the Mt Vesuvius volcano and the Campi Flegrei caldera and suggests a long-lasting prehistoric settlement pattern based on extensive plough-based agriculture dating between at least c. the 3rd millennium BC Agnano Monte Spina eruption and the Middle Bronze Age (1620-1430 cal BC) AP2 eruption. This paper discusses the management of the Early Bronze Age field system at Gricignano d’Aversa US Navy base, Caserta province, on the basis of its topography sealed by the Pomici di Avellino eruption debris (1950-1820 cal BC), here reconstructed for the first time, suggesting that the gullies of the field system were designed for drainage. Settlement and funerary evidence, together with local environmental reconstruction and comparison with similar European contexts, are also employed to infer the social arrangement behind the prehistoric Campanian Plain evidence discussed.

Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode with dual polarizations in VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) were obtained. interferometric SAR (InSAR) processing was conducted to derive coherence between each pair of SAR images acquired consecutively in time throughout the year. Crop seeding and harvest dates were determined by analyzing the time-series InSAR coherence and SAR backscattering. Variation of SAR backscattering coefficients, particularly the VH polarization, revealed seasonal crop growth patterns. The change in InSAR coherence can be linked to change of surface structure induced by seeding or harvest operations. Using a set of physically based rules, a simple algorithm was developed to determine crop seeding and harvest dates, with an accuracy of 85% (n = 67) for seeding-date identification and 56% (n = 77) for harvest-date identification. The extra challenge in harvest detection could be attributed to the impacts of weather conditions, such as rain and its effects on soil moisture and crop dielectric properties during the harvest season. Other factors such as post-harvest residue removal and field ploughing could also complicate the identification of harvest event. Overall, given its mechanism to acquire images with InSAR capability at 12-day revisiting cycle with a single satellite for most part of the Earth, the Sentinel-1 constellation provides a great data source for detecting crop field management activities through coherent or incoherent change detection techniques. It is anticipated that this method could perform even better at a shorter six-day revisiting cycle with both satellites for Sentinel-1. With the successful launch (2019) of the Canadian RADARSAT Constellation Mission (RCM) with its tri-satellite system and four polarizations, we are likely to see improved system reliability and monitoring efficiency.

Soybean yield prediction from UAV using multimodal data fusion and deep learning

Preharvest crop yield prediction is critical for grain policy making and food security. Early estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping and precision agriculture. New developments in Unmanned Aerial Vehicle (UAV) platforms and sensor technology facilitate cost-effective data collection through simultaneous multi-sensor/multimodal data collection at very high spatial and spectral resolutions. The objective of this study is to evaluate the power of UAV-based multimodal data fusion using RGB, multispectral and thermal sensors to estimate soybean (Glycine max) grain yield within the framework of Deep Neural Network (DNN). RGB, multispectral, and thermal images were collected using a low-cost multi-sensory UAV from a test site in Columbia, Missouri, USA. Multimodal information, such as canopy spectral, structure, thermal and texture features, was extracted and combined to predict crop grain yield using Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Support Vector Regression (SVR), input-level feature fusion based DNN (DNN-F1) and intermediate-level feature fusion based DNN (DNN-F2). The results can be summarized in three messages: (1) multimodal data fusion improves the yield prediction accuracy and is more adaptable to spatial variations; (2) DNN-based models improve yield prediction model accuracy: the highest accuracy was obtained by DNN-F2 with an R2 of 0.720 and a relative root mean square error (RMSE%) of 15.9%; (3) DNN-based models were less prone to saturation effects, and exhibited more adaptive performance in predicting grain yields across the Dwight, Pana and AG3432 soybean genotypes in our study. Furthermore, DNN-based models demonstrated consistent performance over space with less spatial dependency and variations. This study indicates that multimodal data fusion using low-cost UAV within a DNN framework can provide a relatively accurate and robust estimation of crop yield, and deliver valuable insight for high-throughput phenotyping and crop field management with high spatial precision.

Penerapan Teknologi Budidaya Inpago Unsoed 1 di Kelompok Tani Padi Desa Tinggarjaya, Kecamatan Jatilawang, Kabupaten Banyumas

Tegaljaya and Mekarjaya are farmer groups in Tinggarjaya Village, Jatilawang District, Banyumas Regency. The narrow land area causes the welfare of farmer groups members to be still low. In order to improve well-being, it is necessary to increase rice productivity using high yield and high quality varieties, namely Inpago Unsoed 1. The purpose of this activity is to increase rice production in Tinggarjaya Village through the application of Inpago Unsoed 1 cultivation technology. This activity was carried out through the Inpago Unsoed 1 Rice Crop Management Field School approach which was translated into three activities, namely 1) Technology Transfer; 2) Plotting Demonstration; and 3) Evaluation of activities. The results of this activity were farmers of the Tegaljaya and Mekarjaya Farmers who received the Inpago Unsoed 1 superior variety and were willing to replan and the farmer gets a new superior variety alternative, Inpago Unsoed 1 which has good competitiveness.