Specific Crop Research Articles

An analysis of net farm income to guide agricultural policies in the Ethiopian highlands

BackgroundAs part of a larger food security project under Ethiopia’s Agricultural Growth Program (CASCAPE), 928 farms in the Ethiopian Highlands were surveyed between 2012 and 2017. The aim was to determine whether the Net Farm Income (NFI) is a relevant indicator that drives food security at the household and the farm level, and to determine its drivers across six study regions of Ethiopia (i.e., Addis Ababa, Hawassa, Haramaya, Bahir Dar, Jimma, and Mekelle). The effect of different socio-economic and environmental drivers on NFI was determined using descriptive statistics, correlation analysis, k-means clustering and comparison of high and low NFI quartiles per region.ResultsThe average annual NFI in Ethiopia was just below 1000 US$ per farm household, with Addis Ababa region leading. Jimma and Bahir Dar were just above average, and the others were at the lower end. In the correlation analysis, NFI was best explained by farm size, net cash flow and the use of nitrogen fertilizer. Male-headed households earned considerably more than female-headed households. The k-means clustering yielded two major farm types on the basis of significant differences in rainfall, farm size, education level, crop diversity, cash flow and N fertilizer use. An analysis of richest 25% versus poorest 25% per region showed Addis Ababa, Bahir Dar, Jimma and Mekelle regions all had significant differences between the two quartiles in farm size, crop diversity and N fertilizer use, whereas Hawassa and Haramaya regions seem more homogeneous.ConclusionsThe survey results present new entry points for informed decision making through targeted, area-specific food security policies in the Ethiopian Highlands by virtue of insight in the regional spread of NFI and its driving forces. Important deductions from the results are policy actions that are obtainable from the results. For example, the farm-size variable provides an indicator on the type of policy action that is required to determine the farm sizes that generate sufficient returns on the overall farming investment. Next, cash-flow is a variable that speaks to the idea on the amount of hard-cash needed by a household to enable it get meaningful returns on cash invested on farming, or a guaranteed minimum return on any specific crop(s) or animal production. Nitrogen fertilizer as an analysis variable is predominantly a crop productivity indicator. In order for the farming to be sustainable, there is need for policy articulation on the amount of nitrogen required for specific yields and crops. Finally, location and rainfall parameters require recommendations on location specific crop management policies that correspond to the rainfall amount, soil types, ecological zones and distance from the markets as maybe gleaned from the results.

Genomic decoding of breeding history to guide breeding-by-design in rice.

Deciphering the intrinsic molecular logic of empirical crop breeding from a genomic perspective is a decisive prerequisite for breeding-by-design (BbD), but remains not well established. Here, we decoded the historical features of past rice breeding by phenotyping and haplotyping 546 accessions covering the majority of cultivars bred in the history of Northeast China (NEC). We revealed that three groups founded the genetic diversities in NEC rice with distinct evolution patterns and traced and verified the breeding footprints to known or genome-wide association study (GWAS)-detected quantitative trait loci (QTLs), or introgressions from indica sub-species with chronological changes in allele frequencies. Then we summarized a rice breeding trend/principle in NEC, and combined with the successful example in breeding and application of Zhongkefa5 to demonstrate the guiding value of our conclusion for BbD in practice. Our study provides a paradigm for decoding the breeding history of a specific crop to guide BbD, which may have implications in different crop breeding.

Irrigation by Crop in the Continental United States From 2008 to 2020

AbstractAgriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use (IWU) by crop. The goal of this study is to estimate crop‐specific IWU from surface water withdrawals (SWW), total groundwater withdrawals (GWW), and nonrenewable groundwater depletion (GWD). To do this, we employ the PCR‐GLOBWB 2 global hydrology model to partition irrigation information from the U.S. Geological Survey Water Use Database to specific crops across the Continental United States (CONUS). We incorporate high‐resolution input data on agricultural production and climate within the CONUS to obtain crop‐specific irrigation estimates for SWW, GWW, and GWD for 20 crops and crop groups from 2008 to 2020 at county spatial resolution. Over the study period, SWW decreased by 20%, while both GWW and GWD increased by 3%. On average, animal feed (alfalfa/hay) uses the most irrigation water across all water sources: 33 from SWW, 13 from GWW, and 10 km3/yr from GWD. Produce used less SWW (43%), but more GWW (57%), and GWD (27%) over the study time‐period. The largest changes in IWU for each water source between the years 2008 and 2020 are: rice (SWW decreased by 71%), sugar beets (GWW increased by 232%), and rapeseed (GWD increased by 405%). These results present the first national‐scale assessment of irrigation by crop, water source, and year. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).

Prediction of Cereal Rye Cover Crop Biomass and Nutrient Accumulation Using Multi-Temporal Unmanned Aerial Vehicle Based Visible-Spectrum Vegetation Indices

In general, remote sensing studies assessing cover crop growth are species nonspecific, use imagery from satellites or modified unmanned aerial vehicles (UAVs), and rely on multispectral vegetation indexes (VIs). However, using RGB imagery and visible-spectrum VIs from commercial off-the-shelf (COTS) UAVs to assess species specific cover crop growth is limited in the current scientific literature. Thus, this study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). UAV surveys were conducted at two fields in Indiana and evaluated five visible-spectrum VIs—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Greenness (ExG). This study utilized simple linear regression (VI only) and stepwise multiple regression (VI with weather and geographic data) to produce individual models for estimating CR biomass, C, N, P, K, and S concentration and content. The goodness-of-fit statistics were generated using repeated K-fold cross-validation to compare individual model performance. In general, the models developed using simple linear regression were inferior to those developed using the multiple stepwise regression method. Furthermore, for models developed using the multiple stepwise regression method all five VIs performed similarly when estimating concentration-based CR variables; however, when estimating content-based CR variables the models developed with GLI, MGRVI, and RGBVI performed similarly explaining 74–81% of the variation in CR data, and outperformed VARI and ExG. However, on an individual field basis, MGRVI consistently outperformed GLI and RGBVI for all CR characteristics. This study demonstrates the potential to utilize COTS UAVs for estimating in-field CR characteristics; however, the models generated in this study need further development to expand geographic scope and incorporate additional abiotic factors.

ADOPTION OF RIVERBED FARMING TECHNOLOGIES IN KAMALA RIVER BASIN, SIRAHA DISTRICT, NEPAL

The study was conducted in the Kamala River basin areas, stretches over three municipalities namely Karjanha, Kalyanpur and Siraha of Siraha district, Nepal, in 2020 to assess the adoption of riverbed farming technologies. Descriptive and statistical tools were used among 179 households selected from 336 sampling population of riverbed farmers. The proportionate stratified random sampling procedure was employed with structured interview schedule for Key Informants Interview (KII) and Focus Group Discussion (FGD). Findings portrayed riverbed farming in Kamala River found to be highly profitable and economically viable with B: C ratio 1.51 and 1.47 for watermelon and cucumber respectively. Similarly, the riverbed farm practices adopted were improved seeds varieties, recommended seed rate, specific crop spacing, recommended fertilizer dose, mulching, use of micronutrient, wind barrier, 3G technology, intercropping, sprinkler/drip irrigation, harvesting technology and post- harvest technology. Among them, ‘specific crop spacing’ was highly adopted followed by use of micronutrient and post-harvest technology whereas ‘sprinkler/drip irrigation’ was least adopted followed by recommended fertilizer dose and mulching technology. The average adoption index was 43.67. Based on the technology adoption, the majority of respondent (64 %) were medium adopters followed by high adopters and then low adopters. In addition, there was a substantial positive correlation between age, sex, education level, mass media, subsidies received, interactions with extension agents, riverbed income, and farming experiences and a large negative correlation between market distance and adoption. While accelerating the implementation of technology in farming systems, socioeconomic variables influencing the adoption of better farming techniques may be considered.

Study of integrated optical and synthetic aperture radar-based temporal indices database for specific crop mapping using fuzzy machine learning model

For specific crop mapping, temporal data is used to handle the spectral overlap between classes. But a number of times, availability of temporal optical remote sensing data is not consistent with the requirements. In this study, a medicinal crop, psyllium husk, was mapped in Jalore district of Rajasthan, India while trying to integrate optical temporal data with synthetic aperture radar (SAR) data. Vegetation indices derived from optical and SAR data, i.e., modified soil adjusted vegetation index 2 (MSAVI2) and radar vegetation index (RVI), respectively, were used to prepare temporal indices databases. Three different temporal layer combinations of MSAVI2 and RVI layers, corresponding to different phenological stages of the target crop, were studied to determine the efficacy of different sensors for crop mapping. The outputs were assessed on the basis of classification efficiency with mean membership difference and variance for determining the effect of heterogeneity in the mapped fields. The results were compared with those obtained using only optical temporal data. From the results obtained, it was observed that the introduction of SAR-based vegetation index as a temporal layer gave good results for fully vegetated and harvest stages of the target crop. The variance within mapped fields was observed as low as 0.0003.

Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms

Timely and accurate estimation of soil moisture content (SMC) is essential for precise irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing with a high spatiotemporal resolution has become a promising method for SMC monitoring. Many existing SMC models have only been tested at a specific crop growth stage using a single type of sensor, and the effects of growth stage and irrigation variation on SMC estimation accuracy remain unclear. To address these limitations, this study used UAV-based multimodal data to quantify SMC in a maize field under various levels of irrigation over two years using three machine learning algorithms (MLA): partial least squares regression (PLSR), K nearest neighbor (KNN), and random forest regression (RFR). The results demonstrated that multimodal data fusion improves the SMC estimation accuracy regardless of the MLA, especially the joint use of thermal and multispectral data. Among three SMC regression models, the RFR model produced the most accurate SMC estimation for the two growing seasons regardless of sensor combinations. The RFR model using all three types of data generated the most accurate and robust SMC estimation at the vegetative stage with R2 of 0.68 and 0.78, and rRMSE of 20.82% and 19.36% for 10- and 20-cm soil depths, respectively; it also produced the best SMC estimation accuracy under well-watered and mild to modest deficit irrigation treatments for both soil depths. The study shows that the high spatial–temporal maps of SMC using UAV-based multimodal data has promising potential for supporting decision-making in irrigation scheduling at the farmland scale.

Handling heterogeneity within castor crop fields through “Individual Sample as Mean” training parameter approach

The high temporal resolution data has a great opportunity for specific crop mapping. The recent advancement in remote sensing data had a good impact on crop classification and mapping. In this research work, modified possibilistic c-means (MPCM) fuzzy machine learning approach has been applied for castor crop mapping, using sentinel 2A/2B satellite temporal images and local convolution as adaptive modified possibilistic local information c-means (ADMPLICM) algorithm was applied to remove isolated unwanted pixels. In training sample parameter “individual sample as mean” method was used to handle heterogeneity within the castor fields. The class-based sensor independent normalized difference vegetation index (CBSI-NDVI), normalized difference vegetation index (Red-NIR), and modified NDVI were produced, then separability analysis was conducted for the selecting optimum temporal date for castor crop mapping. The heterogeneity effect within the field was studied using variance parameters, and its values were observed low within the castor crop field. While considering the “individual sample as mean” training parameter approach on CBSI-NDVI, the variance of the target was decreased from 0.019 to 0.016. The identified castor field variance was close to the training field based on their high membership values and these fields had low mean membership difference (MMD) value. MMD values of castor training class with other crop classes were very high (0.425 – Isabgol, 0.502 – wheat, 0.259 – Fenugreek, 0.809 – Mustard, and 0.569 – Cumin), which indicates other crops were distinguished very well.

Lithothamnion sp. como bioestimulante no cultivo de plantas

ABSTRACT One of the various seaweed species with biostimulating properties is Lithothamnion sp., a calcareous seaweed recognized for its nutritional attributes. This review article aimed to gather information on Lithothamnion sp. and its applications in agricultural cultivation, focusing on identifying and analyzing its biostimulant effects. The practical use of this biostimulant in farming has been confirmed by studies highlighting its efficiency, which varies according to the source material (deposit and particle fraction), application methodology (dosage, methods and frequency) and specific crop (genotype and development stages). Lithothamnion sp. is notable for promoting vegetative growth and has established itself as an invaluable biostimulant in producing seedlings of various species. Its application, either via soil or by foliar methods, has led to improvements in the yield and quality of vegetables, fruits, oilseed crops, grains and forage plants. Although the underlying mechanisms need further investigation, the results suggest that Lithothamnion sp. contributes to amplifying photosynthesis, water-use efficiency and phytoalexin production.

Management of Cover Crop Intercropping for Live Mulch on Plant Productivity and Growth Resources: A Review

Cover crops are a valuable tool for improving resources use, soil health, and productivity. However, the effects of intercropping cover crops as live mulches with cash crops can vary depending on the species, management practices, and environmental conditions. This study reviewed the literature on cover crop intercropping and identified key factors to consider for improved plant productivity and resources use when managing these cropping systems. Science Direct, Scopus, and Google Scholar were used to search for literature on managing cover crop intercropping as live mulches. Research has shown that annual cover crops are typically used in annual field crop systems, while perennial cover crops are typically used in orchards and vineyards. The effects of intercropping annual or perennial cover crops in vineyards, orchards, and field crop systems can vary from positive to negative, depending on the climate, soil, management, and production system. Therefore, there is no one-size-fits-all management strategy. However, there are some key factors that should be considered when managing cover crops, such as: compatibility, intercropping time, planting density, and termination time. The benefits of appropriate cover crop management include: extended growing season, increased soil fertility, resource use optimization, and increased biomass productivity. Cover crop intercropping can be a successful way to improve resource use, soil health and productivity, but it is important to carefully consider the specific crop and management practices to ensure success. Therefore, future research should optimize cover crop intercropping time and planting density in maize-based rainwater harvesting systems on productivity and resource use.

How are genetically modified foods discussed on TikTok? An analysis of #GMOFOODS

Social media is an eminent form of expression of views on all topics, including dietary choices. We set out to look at what aspects of genetically modified foods (GMFs) drive the most engagement on the #GMOFOODS TikTok hashtag. Our main hypotheses were that content focused on the negative aspects of genetically modified organisms (GMOs) would receive more interaction and would be highly driven by consumers rather than professionals. We used videos that were viewed a total of 2,933,577 times collectively by TikTok users. Consumers had uploaded nearly all videos in our sample (n = 91). The most common content category was disadvantages for nutrition and health, noted in 71% of videos, followed by featuring an example of a specific GMO food or crop (68%). All categories that mentioned benefits of GMOs were low (4-6%). Mean number of views were significantly lower for videos that contained information on unintended biological effects (ecology, species of animals, insects or plants, gene transfer) (p = 0.05). Mean number of comments were significantly lower for videos that contained uncertainty of long-term effects on the human population (p = 0.01). Mean number of shares were significantly lower for videos that contained information on unintended biological effects (p = 0.03), antibiotic resistance, toxicity, allergenicity (p = 0.05), and environmental sustainability (p = 0.03). Further research is needed to determine TikTok influences on consumer food choices.

Shelter and shade for grazing sheep: implications for animal welfare and production and for landscape health

Shade and shelter may provide protection from cold and heat stress, a source of feed during prolonged or seasonal drought, specific essential nutrients, increased pasture and crop production and improved landscape health. Cold stress contributes to the average of 8% (single) and 24% (twin) of lambs that die within 3 days of birth in Australia and the estimated 0.7% of the Australian flock that die post-shearing during extreme or unseasonal weather. Shelter has resulted in an average reduction in mortality of 17.5% for twin-born lambs and 7% for single-born lambs according to Australian studies and decreases the susceptibility of ewes to metabolic disease and possibly dystocia. Because many of the published studies are from research areas where cold stress is expected, they are not indicative of industry-wide responses, a research priority is to determine the probability of lamb and ewe deaths from cold stress across different sheep production areas. Although shelter may improve lamb survival, ewes do not always choose to lamb in a sheltered location. For this reason, there is a requirement for research into the voluntary use of shelter in commercial-sized paddocks and the role that nutritive value of shelter plays in attracting and holding ewes to shelter, and to their lambs. Heat stress may also result in lamb deaths and influences feed conversion efficiency, appetite, reproduction, wool growth and disease susceptibility. The consequences of heat stress may go unnoticed over a yearly production cycle, although there is some evidence that shade may increase weaning rates and feed intake of grazing sheep. There are ancillary benefits from shade and shelter. Trees may improve crop production through reducing wind damage and evapotranspiration and provide timber. Shrubs provide feed during the summer–autumn feed gap or drought, are useful for the management of land degradation and provide habitat for native fauna. It is clear that shade and shelter in the correct locations provide a range of benefits to livestock and the landscape; nevertheless, adoption appears low. Research that focuses on defining the benefits on a farm or landscape scale is required to support extension programs.

The financial value of seasonal forecast-based cultivar choice: Assessing the evidence in the Central Rift Valley of Ethiopia

Among many other options, seasonal weather forecasts and the use of cultivars that are better adapted to local climate and climate variability have been discussed as two potential supporting measures to assist farmer adaptation to climatic variability and change. In this article, we evaluate the potential benefit of combining these two measures, i.e., choosing specific crop varieties based on seasonal forecasts – focusing on the Central Rift Valley in Ethiopia. We base our value of information analysis on the available records of field trial data for publicly released crop varieties. We find that experimental evidence must be extended and improved in order to provide reliable evidence of yield performance differences between crop varieties, which is an essential prerequisite for exploiting forecast information. Classification of cumulative seasonal rainfall based on the modified Rainfall Anomaly Index provides a sharper distinction than using the standard tercile-based approach employed in the Ethiopian seasonal forecast communication. Even with a fairly optimistic interpretation of the evidence with respect to exploitable yield differences, we find only modest benefits of seasonal forecasts at realistic forecast accuracy for the region. Given the empirical limitations when assembling long-run yield data, the presented results have to be understood as a first approximation. Apart from sufficiently accurate forecasts, success of forecast-based cultivar choice will depend on (i) more reliable evidence on performance differences between crop varieties under different weather conditions, and (ii) changes in the current seed breeding and distribution system in developing countries, because the full potential of high-accuracy seasonal forecast information could only be tapped, if forecast-matching cultivars are being made available to farmers in time.

Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline.

Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.

Diversity of Beneficial Bacteria in Tamhini Ghat Region of Western Ghats, M.S., India

The present study investigates that the isolation of beneficial bacteria from a soil sample collected from the Tamhini Ghat region of the Western Ghats. In October 2022, 15 soil samples were collected from Tamhini Ghat. Five different beneficial bacteria were isolated from these collected soil samples and characterized morphologically. The beneficial bacteria like Azotobacter spp., Bacillus spp., Azospirillum spp., Pseudomonas spp., and Frateuria spp. Isolation of these bacteria was carried out on a specific medium and observed after three days of the incubation period at 28±2 0 C temperature. These isolated bacteria were purified on slants prepared by using a specific medium and stored at 4 0 C for further study and observed under a microscope by performing a Gram staining test. These beneficial bacteria can be used for biofertilizer formulation in future studies. All over India, farmers are using chemical fertilizers, which directly impact beneficial soil microbe that leads to decreased soil health. For improving soil health, it is the need of the hour to integrate the use of chemical fertilizers along with biofertilizers as per the recommended dose of fertilizers (RDF) given by the agricultural universities for the specific crops.

Fertilizer type and quantity recommendation to increase oilseed crops yield prediction with inorganic fertilizers using machine learning algorithms

Agriculture contributes significantly to India’s economy. The most serious threat to food security is population growth. Population growth increases demand, forcing farmers to produce more to increase supply. Crop yield prediction technology can help farmers to increase their output. Optimal fertilizer dose are required for boosting oilseed crop yield cultivation. However, when nutrients are scarce or overfertilization occurs, yields are considerably lowered and the environmental burden is increased. To address these issues, our proposed work employs machine learning techniques in the prediction of crop yield using inorganic fertilizer as well as the amount and type of agricultural fertilizer to be used for a specific crop in various districts of Tamil Nadu. Actual yield data from 1961 to 2007 is used as a training set, and data from 2008 to 2019 is used as a validation set. The results of the proposed algorithm are compared with those of the other machine learning algorithms namely random forest, linear regression, support vector machine, and naive bayes with an accuracy rate of 94%, 91.33%, 88.4% and 75.56% respectively are observed. According to the study, random forest results outperform other algorithms for crop yield prediction, and the decision tree algorithm works better for recommendation systems. The research also helps farmers by providing a recommendation system for determining which crop to plant and which type of inorganic fertilizer and how much quantity of fertilizer to use in a specific area and time. The proposed study also seeks to examine different observations for each method by changing parameters to see if the varying parameter influences the accuracy rate or not.

Design and technological scheme of a multifunctional seeding unit

Currently, the vast majority of agricultural producers in the Southern Federal District with a total area of arable land up to 50-70 hectares cultivate three main crops, namely grain ears, usually winter wheat, and row crops – corn for grain and sunflower. Grain ear crops are sown in the usual ordinary way, and row crops use the dotted method. These key agrotechnological features lead to the need to use different sowing machines equipped with different types of sowing units. Since the vast majority of agricultural producers of the Southern Federal district with a total area of arable land up to 50-70 hectares do not use monoculture in the structure of crops, this leads to a low annual load of these seeders. This is through deductions for depreciation, maintenance service and repairs inevitably affect the final cost of production. It should also be noted that often, due to the high cost of sowing machines, small agricultural producers are not able to purchase the required agricultural machinery, in addition, this is most often not economically feasible. The use of hired units does not allow sowing in the specified agrotechnical terms, which significantly reduces their productivity and quality parameters. The use of specialized seeders leads to an increase in metal consumption and multi-marking on the farm. This problem can be solved by using a multifunctional seeding machine (MSU). This will make it possible to abandon the use of specialized seeders and perform sowing of grain and row crops with the same machine by carrying out conversion for a specific crop. We have de-scribed the design scheme of the MSU and the principle of its operation. The technical result of the development is also presented and the way to achieve is described.

Multi-Parameter Sensor Based Automation Farming

IOT innovation is used in the development of the Smart Farming Tracking the System. An Arduino Uno, a temperature humidity sensor, soil moisture sensor, water level sensor, water pumps, and DC motors strength this system. If the smart farming tracking system turns on, the sensors find the field’s water level and the soil’s moisture level. If the irrigation water stage falls below the level defined for a specific crop grown in the growing area, the irrigation system is going to start to pump water. The IOT warns concerning current level of water, soil moisture stage, and motor beginning will be shown on the LCD panel of the section. We are able to use the pumps by hand via a webpage. The farmers are additionally getting this data via mobile phone. By hitting a system- provided link, the individual using it may firmly prevent the water’s flow within the field. While carried out, the system will assist landowners to preserve suitable soil water and moisture levels, thus boosting yields with little work. The goal of this article is to identify grow illnesses and reduce losses in money. For picture appeal, we suggested an entirely based on deep learning method. We put the three most common Neural Network Designs to the test: Faster Region-based entirely judgment (SVM)Support Vector Machine Region-based entirely (RF) Random Forest method. The method suggested in the research can correctly detect many types of disease and is capable of dealing in complicated situations. In addition, the method may be expanded to recommend fertilizer according to extent evaluation as well as measurement. artificial intelligence (AI) entirely Machine Learning Response to this the combination the issue is a supervised categorization judgment.

A scientometric review of the peer-reviewed research on high tunnels in the United States

Abstract This study assessed the extant literature related to the use of high tunnels (HTs) in agricultural systems in the United States since the 2009 launch of the Natural Resources Conservation Service (NRCS) High Tunnel Initiative. This NRCS program led to an increase in HT adoption nationwide. The literature searches were conducted using the Web of Science (WoS) database. The final sample was 133 peer-reviewed articles published between 2009 and February 2023. We used CiteSpace 6.2.R1 and Gephi 0.9.2 to conduct co-citation, co-author, co-institution, and clustering techniques. The findings showed that the peer-reviewed literature about HT use has increased since 2009, substantially rising between 2017 and 2021. Horticulture was the top subject category in the literature, and most articles were published in peer-reviewed journals of the American Society for Horticultural Science (i.e., HortTechnology and HortScience). The research field evolved from general HT practices, nutrient management, and plant pathology to focus on trials of specific crops and integrated pest management. The institutions with the most contributions to the HT literature were Kansas State University, the University of Florida, Michigan State University, Purdue University, and the University of Minnesota. The patterns of HT research revealed in this study offer a greater understanding of the current state of knowledge to inform the focus of future research.

An Energy-Efficient PAR-Based Horticultural Lighting System for Greenhouse Cultivation of Lettuce

This paper presents an intelligent horticulture lighting and monitoring system to achieve energy-efficient supplemental lighting while maintaining the light quality and intensity at desired levels in the photosynthesis spectrum. Energy-efficiency is achieved through delivering only the required net light intensity, consisting of sunlight and supplemental LED light, using an intelligent controller that does not depend on the lighting system model. To this end, an online neural-network learning control system is developed, comprised of low-cost light sensors for measuring the photosynthetic photon flux density (PPFD), dimmable LED light fixtures, cameras, and internet-of-things (IoT)-enabled firmware used for crop monitoring and performance evaluation. Experiments performed in a research greenhouse facility on the lettuce crop are presented which indicate that the system can deliver the desired Daily Light Integrals (DLIs) to the plants in the presence of changing daylight conditions. The proposed method can thus deliver the exact amount of light to a specific crop based on the required light recipes during different growth phases. The control performance is further compared with a conventional on-off time-scheduling method in terms of plant health, growth, and energy requirements. The experiments indicate that the proposed solution can reduce energy consumption per unit dry mass of lettuce by 28% when compared to existing time-scheduling methods.