Land Suitability and Insurance Premiums: A GIS-based Multicriteria Analysis Approach for Sustainable Rice Production

The purpose of this research is to develop a land suitability model for rice production based on suitability levels and to propose insurance premiums to obtain maximum returns based on the harvest index and subsidy dependence factor for the marginal and moderately suitable lands in the northern part of Bangladesh. A multicriteria analysis was undertaken and a rice land suitability map was developed using geographical information system and analytical hierarchy process. The analysis identified that 22.74% of the area was highly suitable, while 14.86% was marginally suitable, and 28.54% was moderately suitable for rice production. However, 32.67% of the area, which was occupied by water bodies, rivers, forests, and settlements, is permanently not suitable; 1.19% is presently not suitable. To motivate low-quality land owners to produce rice, there is no alternative but to provide protection through crop insurance. We suggest producing rice up to marginally suitable lands to obtain support from insurance. The minimum coverage is marginal coverage (70%) to cover the production costs, while the maximum coverage is high coverage (90%) to enable a maximum return. This new crop insurance model, based on land suitability can be a rational support for owners of different quality land to increase production

Environmental load assessment for an integrated design of microalgae system of palm oil mill in Indonesia

The environmental load of continuous bioenergy production from palm oil (Elaeis guineensis) included with a proposed 10 ha of microalgae production system were assessed to be implemented in Indonesia. Material and energy balances, greenhouse gas (GHG) emission, nutrient requirement and also water scarcity during bioenergy production cycle were evaluated. The integrated system was developed for 60 tons h−1 of fresh fruit bunch (FFB) processing capacity of a conventional mill. Aggregate of energy-profit ratio from the proposed system was 5.20, which indicates a positive balance. The total water footprint for each palm oil and microalgae cultivation was 3.18 and 2.85 m3 kg−1 of biodiesel production, respectively. Microalgae mix-culture has the potential to treat organic compounds from palm oil mill effluent (POME) and combined with flue gases from biomass and biogas power plant as the alternative nutrient sources contributed to net-reduction of GHG emission for 158.8 tons ha−1 of microalgae culture, annually. The integrated system produced 26,471 tons of biodiesel that included 223 tons from microalgae and contribute to 39.90% of total GHG emission reduction from diesel fuel substitute. Additional co-product of 520.33 tons year−1 of animal feed from defatted biomass also possible to be produced and have potential for environmental benefits

Shoreline Change Assessment in the Coastal Region of Bangladesh Delta Using Tasseled Cap Transformation from Satellite Remote Sensing Dataset

Bangladesh is a global south hotspot due to climate change and sea level rise concerns. It is a highly disaster-prone country in the world with active deltaic shorelines. The shorelines are quickly changing to coastal accretion and erosion. Erosion is one of the water hazards to landmass sinking, and accretion relates to land level rises due to sediment load deposition on the Bay of Bengal continental shelf. Therefore, this study aimed to explore shoreline status with change assessment for the three study years 1991, 2006, and 2021 using satellite remote sensing and geographical information system (GIS) approaches. Landsat 5, 7 ETM+, and 8 OLI satellite imageries were employed for onshore tasseled cap transformation (TCT) and land and sea classification calculations to create shore boundaries, baseline assessment, land accretion, erosion, point distance, and near feature analysis. We converted 16,550 baseline vertices to points as the study ground reference points (GRPs) and validated those points using the country datasheet collected from the Survey of Bangladesh (SoB). We observed that the delta’s shorelines were changed, and the overall lands were accredited for the land-increasing characteristics analysis. The total accredited lands in the coastal areas observed during the time periods from 1991 to 2006 were 825.15 km2, from 2006 to 2021 was 756.69 km2, and from 1991 to 2021 was 1223.94 km2 for the 30-year period. Similarly, coastal erosion assessment analysis indicated that the results gained for the period 1991 to 2006 and 2006 to 2021 were 475.87 km2 and 682.75 km2, respectively. Therefore, the total coastal erosion was 800.72 km2 from 1991 to 2021. Neat accretion was 73.94 km2 for the 30-year period from 1991 to 2021. This research indicates the changes in shorelines, referring to the evidence for the delta’s active formation through accretion and erosion processes of ‘climate change’ and ‘sea level rise’. This research projects the erosion process and threatens land use changes toward agriculture and settlements in the coastal regions of Bangladesh

Pear Recognition in an Orchard from 3D Stereo Camera Datasets to Develop a Fruit Picking Mechanism Using Mask R-CNN

In orchard fruit picking systems for pears, the challenge is to identify the full shape of the soft fruit to avoid injuries while using robotic or automatic picking systems. Advancements in computer vision have brought the potential to train for different shapes and sizes of fruit using deep learning algorithms. In this research, a fruit recognition method for robotic systems was developed to identify pears in a complex orchard environment using a 3D stereo camera combined with Mask Region-Convolutional Neural Networks (Mask R-CNN) deep learning technology to obtain targets. This experiment used 9054 RGBA original images (3018 original images and 6036 augmented images) to create a dataset divided into a training, validation, and testing sets. Furthermore, we collected the dataset under different lighting conditions at different times which were high-light (9–10 am) and low-light (6–7 pm) conditions at JST, Tokyo Time, August 2021 (summertime) to prepare training, validation, and test datasets at a ratio of 6:3:1. All the images were taken by a 3D stereo camera which included PERFORMANCE, QUALITY, and ULTRA models. We used the PERFORMANCE model to capture images to make the datasets; the camera on the left generated depth images and the camera on the right generated the original images. In this research, we also compared the performance of different types with the R-CNN model (Mask R-CNN and Faster R-CNN); the mean Average Precisions (mAP) of Mask R-CNN and Faster R-CNN were compared in the same datasets with the same ratio. Each epoch in Mask R-CNN was set at 500 steps with total 80 epochs. And Faster R-CNN was set at 40,000 steps for training. For the recognition of pears, the Mask R-CNN, had the mAPs of 95.22% for validation set and 99.45% was observed for the testing set. On the other hand, mAPs were observed 87.9% in the validation set and 87.52% in the testing set using Faster R-CNN. The different models using the same dataset had differences in performance in gathering clustered pears and individual pear situations. Mask R-CNN outperformed Faster R-CNN when the pears are densely clustered at the complex orchard. Therefore, the 3D stereo camera-based dataset combined with the Mask R-CNN vision algorithm had high accuracy in detecting the individual pears from gathered pears in a complex orchard environment

Fast and Non-Destructive Quail Egg Freshness Assessment Using a Thermal Camera and Deep Learning-Based Air Cell Detection Algorithms for the Revalidation of the Expiration Date of Eggs

Freshness is one of the most important parameters for assessing the quality of avian eggs. Available techniques to estimate the degradation of albumen and enlargement of the air cell are either destructive or not suitable for high-throughput applications. The aim of this research was to introduce a new approach to evaluate the air cell of quail eggs for freshness assessment as a fast, noninvasive, and nondestructive method. A new methodology was proposed by using a thermal microcamera and deep learning object detection algorithms. To evaluate the new method, we stored 174 quail eggs and collected thermal images 30, 50, and 60 days after the labeled expiration date. These data, 522 in total, were expanded to 3610 by image augmentation techniques and then split into training and validation samples to produce models of the deep learning algorithms, referred to as “You Only Look Once” version 4 and 5 (YOLOv4 and YOLOv5) and EfficientDet. We tested the models in a new dataset composed of 60 eggs that were kept for 15 days after the labeled expiration label date. The validation of our methodology was performed by measuring the air cell area highlighted in the thermal images at the pixel level; thus, we compared the difference in the weight of eggs between the first day of storage and after 10 days under accelerated aging conditions. The statistical significance showed that the two variables (air cell and weight) were negatively correlated (R2 = 0.676). The deep learning models could predict freshness with F1 scores of 0.69, 0.89, and 0.86 for the YOLOv4, YOLOv5, and EfficientDet models, respectively. The new methodology for freshness assessment demonstrated that the best model reclassified 48.33% of our testing dataset. Therefore, those expired eggs could have their expiration date extended for another 2 weeks from the original label date

Land Suitability Analysis for Potential Vineyards Extension in Afghanistan at Regional Scale Using Remote Sensing Datasets

Grapes are one of the world’s most widely distributed crops and are cultivated in more than 100 countries in the global scheme. Due to climate change and improper vine growth variable selection, production has significantly decreased across countries. Therefore, the primary purpose of this study was to develop a land suitability analysis method using a fuzzy expert system at a regional scale. The fuzzy membership function was used in the ArcGIS® environment to perform the spatial analysis, and the overlay function was used to generate the final suitability map for Afghanistan considering policy planning. The results indicated that 23% (15,760,144 ha) of the areas were potential and located in the highly suitable region for grape production; however, 11% (7,370,025 ha) of the regions were not suitable for vineyards throughout the country of Afghanistan. In the present study, it was observed that most of the vineyards were in highly suitable areas (90%, 80,466 ha), while 0.01% (5 ha) of the vineyards were in less suitable areas. The present analysis demonstrated that the significant extension of grape vines can be possible in highly suitable areas. The results of this research can support decision-makers, farm managers and land developers to find more prospective acreage for expanding vineyards in Afghanistan

Development of an IoT-Based Precision Irrigation System for Tomato Production from Indoor Seedling Germination to Outdoor Field Production

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production

Climate-Adaptive Potential Crops Selection in Vulnerable Agricultural Lands Adjacent to the Jamuna River Basin of Bangladesh Using Remote Sensing and a Fuzzy Expert System

Agricultural crop production was affected worldwide due to the variability of weather causing floods or droughts. In climate change impacts, flood becomes the most devastating in deltaic regions due to the inundation of crops within a short period of time. Therefore, the aim of this study was to propose climate-adaptive crops that are suitable for the flood inundation in risk-prone areas of Bangladesh. The research area included two districts adjacent to the Jamuna River in Bangladesh, covering an area of 5489 km2, and these districts were classified as highly to moderately vulnerable due to inundation by flood water during the seasonal monsoon time. In this study, first, an inundation vulnerability map was prepared from the multicriteria analysis by applying a fuzzy expert system in the GIS environment using satellite remote sensing datasets. Among the analyzed area, 42.3% was found to be highly to moderately vulnerable, 42.1% was marginally vulnerable and 15.6% was not vulnerable to inundation. Second, the most vulnerable areas for flooding were identified from the previous major flood events and cropping practices based on the crop calendar. Based on the crop adaptation suitability analysis, two cash crops, sugarcane and jute, were recommended for cultivation during major flooding durations. Finally, a land suitability analysis was conducted through multicriteria analysis applying a fuzzy expert system. According to our analysis, 28.6% of the land was highly suitable, 27.9% was moderately suitable, 19.7% was marginally suitable and 23.6% of the land was not suitable for sugarcane and jute cultivation in the vulnerable areas. The inundation vulnerability and suitability analysis proposed two crops, sugarcane and jute, as potential candidates for climate-adaptive selection in risk-prone areas

Land Suitability Analysis for Potential Vineyards Extension in Afghanistan at Regional Scale Using Remote Sensing Datasets

Grapes are one of the world’s most widely distributed crops and are cultivated in more than 100 countries in the global scheme. Due to climate change and improper vine growth variable selection, production has significantly decreased across countries. Therefore, the primary purpose of this study was to develop a land suitability analysis method using a fuzzy expert system at a regional scale. The fuzzy membership function was used in the ArcGIS® environment to perform the spatial analysis, and the overlay function was used to generate the final suitability map for Afghanistan considering policy planning. The results indicated that 23% (15,760,144 ha) of the areas were potential and located in the highly suitable region for grape production; however, 11% (7,370,025 ha) of the regions were not suitable for vineyards throughout the country of Afghanistan. In the present study, it was observed that most of the vineyards were in highly suitable areas (90%, 80,466 ha), while 0.01% (5 ha) of the vineyards were in less suitable areas. The present analysis demonstrated that the significant extension of grape vines can be possible in highly suitable areas. The results of this research can support decision-makers, farm managers and land developers to find more prospective acreage for expanding vineyards in Afghanistan