Quantity Of Agricultural Products Research Articles

FEASIBILITY STUDY ON LIVESTREAMS SELLING OF AGRICULTURAL PRODUCTS ON TIK TOK PLATFORM — A CASE STUDY OF YINGKOU CITY IN LIAONING PROVINCE

Since the beginning of 2020, offline physical stores and the sale of large quantities of agricultural products have all been affected by COVID-19. However, e-commerce in this case could make full use of its advantages of not being restricted by regional space. From 2020 to 2021, many regions and businesses have tried the form of e-commerce livestreams selling. However, as of May 2021, Yingkou city of Liaoning province was still affected by the COVID-19, resulting in a large number of unsalable agricultural products. The daily unsold volume of three towns in Bayuquan district and five towns in Gaizhou city was nearly 400 tons, which undoubtedly caused huge losses to the local farmers. This situation also indicated how little local farmers knew about the mode of livestreams selling. As the leader of short video platforms, Tik Tok has been studied by a large number of scholars on its operation mode, selling mode and cash ability. Because each region has many problems such as differences in urban factors, refined research is more suitable for these regions. For this reason, it is of certain significance to study the livestreams selling of agricultural products on Tik Tok platform of Yingkou city in Liaoning province.

Dobra poljoprivredna praksa u primeni sredstava za zaštitu bilja

Agriculture is the world's second largest source of employment, often involving long hours and heavy work in challenging climates and harsh conditions. The continued development of sustainable intensive agriculture is essential to maintain the quality and quantity of agricultural products for consumers, particularly with the scarcity of natural resources, climate change, a growing world population and the physical limitation of productive agricultural land. On farm best management practices (BMPs) during transport, storage and use of crop protection products (CPP) help to avoid the contamination of operators, soil and water bodies. Also, the correct disposal of empty CPP containers and remnants from spraying is crucial to avoid point source contamination of water bodies. BMPs include wearing appropriate personal protective equipment to prevent direct contact with crop protection products. When BMPs are in place for handling and using PPPs and product spills are managed properly, the risk of contamination both to humans and the environment will be reduced significantly. Farmers around the world should be aware that land and water management are critical for agriculture and ecosystems. On-farm water management optimizes water use for crops and other farm operations. Spray drift, drain flow, run off and leaching from fields may cause contamination of surface and ground water. The adoption of best management practices, such as field margins, optimized irrigation systems and best in class application technology, will help to prevent such contamination as well as improve agricultural productivity. Significant surface water contamination can also occur as a result of improper filling and cleaning of spray equipment. It is simple and easy to prevent by using separated based systems to collect this and other waste water. In separation based systems, waste water is put in a container. Crop protection products are then physically separated from the water, e.g. by evaporating the water into the atmosphere with the help of the wind and sun, which leaves the residues of crop protection products in the container.

Influence of meteorological conditions on the residual content of pesticides in plants

The quality and quantity of agricultural products depends on the weather conditions in the regions of cultivation. The results of the influence of weather on the degradation rate of pesticides in agricultural products are presented in this paper. Samples were analyzed for the content of pesticide active ingredients by various chromatographic methods. Air temperature has a significant effect on the intensity of metabolic processes in the plant, which determine the degradation rate of pesticides. Air humidity is more important for water-soluble pesticides than for lipophilic ones. Rain, moisture and dew accelerate the dispersal of substances from the aboveground plant parts. When choosing pesticides to protect crops from harmful organisms, it is necessary to take into account the natural and climatic characteristics of the region.

Determination of Fenobucarb and Chlorbenside in Wastewater and Lake Water Samples by Gas Chromatography Mass Spectrometry

The advent of pesticide production on large scale has contributed immensely to the quality and quantity of agricultural products, and averting diseases that stem from pest vectors. Despite these benefits, inappropriate or excessive usage of these chemicals could have negative impacts on humans and other organisms in the environment. Thus, this study was designed to detect and quantify fenobucarb and chlorbenside in wastewater and lake water samples by a gas chromatograph system coupled to a mass selective detector. A suitable temperature program was used to elute the two analytes at distinct retention times, and satisfactory system performance values were obtained under optimum conditions. All standard solutions and spiked samples were gravimetrically prepared in order to enhance the accuracy of quantification. The analytes were not detected in the two test samples, but spike recovery experiments were performed to validate the accuracy of the method. Percent recoveries calculated using the external calibration method were below 75%. Thus, matrix matching calibration method was used to mitigate matrix effects to obtain approximately 100% recovery results for five different spike concentrations.

Leaf Disease Detection

Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, also reduction in both quality and quantity of agricultural production. Leaves are important for fast growing of plant and to extend production of crops. Identifying diseases in plant leaves is challenging for farmers and also for researchers. Currently farmers are spraying pesticides to the plants but it affects humans directly or indirectly by health or also economically. To detect these plant diseases many fast techniques got to be adopt. In this paper, we have done surveys on different leaf diseases and various advanced techniques to detect these diseases. As said by Mahatma Gandhi, "Agriculture is the backbone of the Indian Economy". Hence the detection of leaf diseases is an important aspect in increasing the yield of a crop. By detecting the leaf disease farmer can increase the crop yield which leads in growth of country’s economy.

Plant Leaf Disease Prediction

The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challenging to monitor manually as it requires a great deal of work, expertise on plant diseases, and excessive processing time. Hence, this can be achieved by utilizing image processing techniques for plant disease detection. These techniques include image acquisition, image filtering, segmentation, feature extraction, and classification. Convolutional Neural Network’s(CNN) are the state of the art in image recognition and have the ability to give prompt and definitive diagnoses. We trained a deep convolutional neural network using 20639 images on 15 folders of diseased and healthy plant leaves. This project aims to develop an optimal and more accurate method for detecting diseases of plants by analysing leaf images.

Deep Learning for Leaf Disease Detection using CNN

Agriculture is the backbone of the Indian economy. About 70% of people rely on it and share a large portion of GDP. Diseases in plants especially in the leaves affect the reduction of both quality and quantity of agricultural products. The human eye is not so powerful to detect minute differences in the infected part of the leaf. In this paper, we offer a software solution to automatically detect and diagnose plant leaf diseases. In this we use image processing techniques for the diagnosis and early diagnosis can be done as elsewhere. This approach will improve crop production. It involves several steps. Picture detection, pre-image processing, percentage separation, extraction and neural features. Recently, many researchers have advocated since the success of in-depth computer literacy the idea of improving the effectiveness of diagnostic programs. Unfortunately, most of these studies did not use the latest in-depth formats and were based on AlexNet, GoogleNet or similar properties. Moreover, deep-seated mechanisms do not exist to take advantage of it, making these deep divisions invisible and suitable as black people boxes. In this project, we tested the many technological approaches of the Convolutional Neural Network (CNN) buildings using various learning strategies in the public database of plant diseases separation. These new structures exceed the high-quality effects of plant diseases in stages with very high accuracy. In addition, we have suggested the use of saliency maps as a way to visualize and interpret CNN classification.

Marketing potential of the Sino-Russian bilateral agricultural export market

China and Russia are important agricultural countries in the world. Expanding exports and increasing sales of agricultural products play an important role in the economic development of both countries. To understand the current situation of agricultural exports of the two countries and formulate strategies to expand the marketing of agricultural products, this paper uses the UN Comtrade Database 2009-2018 on Chinese and Russian bilateral agricultural export sales and other trade data to calculate the (expansion margin) and (price margin) of agricultural exports, (quantity margin), to analyze the types, prices, and quantities of exported agricultural products. The results show that China exports to Russia mainly labor-intensive types of agricultural products such as processed agricultural and horticultural products, accounting for 87.46% of total agricultural exports on average. The increase in exports is mainly due to the continuous increase in the prices of exported agricultural products. Russia exports to China mainly land-intensive types of agricultural products such as animal products, grains, oilseeds and fat products, which accounted for an average of 79.07% of total agricultural exports. The increase in exports was mainly due to the continuous increase in types and quantities of agricultural products to develop the export potential of agricultural products and expand sales. In addition, China should expand the types and quantities of agricultural products exported, and Russia should increase the added value of agricultural products and raise the export prices of agricultural products.

Automated Quality Assessment of Crops Using CNN - Keras

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

Analyze the Effectiveness of the Algorithm for Agricultural Product Delivery Vehicle Routing Problem Based on Mathematical Model

With the recent development in the economic system, the requirement for logistic services has also increased gradually. This increased the demand for efficient and cost-effective delivery services without compromising the quality and timeliness. This has become a challenge to the logistic service providers to maintain the high-quality standards along with reliable delivery services. A mathematical equation model is proposed in this work to solve the problem of random quantity of agricultural products collected/distributed by working vehicle collection/distribution path planning. This article proposes a hybrid algorithm which combines the taboo algorithm search and the taboo hybrid algorithm to solve the problem. In the proposed algorithm, a large-scale problem is several small-scale problems to reduce the time complexity of the algorithm. Since randomness is much more complicated than certain types of problems, accurate algorithms can only be applied to a small range of problem types. The heuristic calculations involved in the development of algorithms make it a convenient simplified tool for the collection and distribution of random agricultural products. An average validation accuracy of 94% has been obtained for the proposed algorithm after completing 200 iterations while obtaining 94.37%, 94.57%, and 94.56% precision, recall, and F-score values, respectively.

Canker Detection in Citrus Plants with an Efficient Finite Dissimilar Compatible Histogram Leveling Based Image Pre-processing and SVM Classifier

The massive loss in agricultural yield mainly caused by leaf and fruit diseases. These diseases reduce both quality and quantity of agricultural products. As a major provenience of nutrients like vitamin C, citrus plants such as lemons, mandarins, oranges, tangerines, grapefruits, and limes are commonly grown fruits all over the world. As a result of various plant diseases, citrus producing companies make a huge amount of waste every year whereby 50% of citrus peel is destroyed every year. The disease canker is one of the mentionable leaf and fruit diseases. The main goal of this paper is to recognize and classify the canker disease precisely from the contrived leaf images by employing image processing techniques to detect plant leaf diseases from digital images. We offer a method consists of two phases to enhance the clarity of leaf images. The primary stage uses Finite Dissimilar Compatible Histogram Leveling (FDCHL) in preliminary step which advances the dissimilar level of disease influenced leaf image, segment the region of interest using fuzzy feature selection. The second phase by adopting the Support Vector Machine classifier to find out the canker leaf image and implements these methods in lemon citrus canker disease identification. Experimental results show effective accuracy detection and reduced execution time of canker disease detection.

Hydroponic: Empowering Local Farmer Knowhow to Gain Value Added on Agriculture Commodity

The agricultural sector is one of the potential commodities in business at domestic and abroad, local farmers in the West Java area are trying to run the best farming system that is adapted to market needs. Conventional farming systems that require large tracts of land, the availability of farmer labor and the length of time for harvesting are obstacles for local farmers to expand their businesses and increase their production yields. A concept in the agricultural system that is different from conventional agriculture yet can be used by local farmers at this time is hydroponic farming. Hydroponic farming that uses water as a planting medium can be implemented anywhere such as urban areas because it does not require large tracts of land. One of the hydroponic techniques that is currently developing is the NFT or Nutrient Film Technique which can be applied in the highlands and lowlands. This system can provide quality harvests. The NFT system provides added value for farmers by increasing farmers' knowledge about the hydroponic farming system and increasing agricultural commodities produced, through this system the farmers can run their farming business on land that is not extensive in urban areas. The commodities produced are of high quality because they are environmentally friendly, with efficient fertilizer, water and the use of non-pesticide ingredients. In fact, this hydroponic system requires a large enough cost to purchase material, regardless of the greater quantity of harvest and shorter harvest time. Increasing the quality and quantity of agricultural products encourage local farmers to run a hydroponic system in meeting market needs so that they have high economic value and can improve the welfare of local farmers.

Climate adaptive agricultural extension approaches for putting research into use

The reality of climate change is harsh and it’s a bitter truth for most of our farmers. In the present situation, climate change is one of the most important challenges to food security. Pertinent actions are, therefore, needed to address these challenges to enable the agricultural sector to significantly contribute to the attainment of agriculture development. Climate adaptive agricultural approach is an approach for developing agricultural strategies to secure sustainable food security under climate change. Through CAA the quality and quantity of agricultural production can be increased and production costs can be decreased. For example, with climatologically data it is also possible to recognize bad weather conditions and to be more prepared to minimize the damage. Many projects have recently adopted with the idea of climate smart agriculture for mitigating the challenges of climate change. With the help of suitable extension approaches we can effectively disseminate various climate smart agricultural practices to farming community.

SMART FARMING MANAGEMENT SYSTEM USING IOT

Smart agriculture farming system is a new idea of farming in agriculture, because which uses IoT technology to monitor the crop 24/7 and sends the information to the cloud. This emerging system increases the quality and quantity of agricultural products. IoT technology provides the information about farming fields and then takes action depending on the farmer input. In this paper, we can design an IoT based advanced solution for monitoring the soil conditions and atmosphere for efficient crop growth is presented. The developed system is capable of monitoring temperature, humidity, soil moisture type using NodeMCU and different sensors connected to the microcontroller. Also, a notification is shown in farmer’s phone using Wi-Fi about environmental condition and water levels of the crop field

Plant Disease Detection and Suggestions using Mobile App

70% of rural households depend on agriculture as a livelihood but farmers have never really benefited from the advancements in technology. As of 2020, agriculture is responsible for 19.9% of India’s GDP. Therefore in a country like India, damage to the agricultural produce due to plant and crop diseases can be very crucial. The purpose of agriculture is not only to contribute to the economy and feed the ever increasing population but it is a major source of energy and a solution to solve the problem of global warming. Due to plant diseases the quality and quantity of agricultural products is affected adversely. If proper precautions are not taken the farmer can lose an entire seasonal cultivation. Some of the plant diseases do not have visibility during early stages; they only appear at that last stage. Plant disease diagnosis is very important in earlier stages in order to treat and contain the disease as it can damage the entire cultivation. Depending on the needs of the farmer, a system that uses Machine Learning, image classification has been proposed.

Deformation processes in open drainages

The quality and quantity of agricultural products depend on land reclamation. Collector-drainage systems are used to improve the reclamation state of irrigated areas. The article discusses the stability of slopes of open collector-drainage networks in the irrigated lands of the Hungry Steppe. Discussed problems create during construction and providing productivity of drainages system in irrigation fields. The causes of the deformation process are discussed. The deformation process occurring as a result of landslides of the banks of open collectors is estimated. Recommendations for determining the stability of the banks of an open collector are based on the definition of a model of circular cylindrical sliding surfaces. Used statistic law of mechanics in calculation work based on the model of the ground movement around a cylindrical surface. During calculations, the main attention is given to the weight force, hydrodynamic force, and bite force of the ground. Stability coefficient calculated according to keeping moment (М1) moving moment (М2). In determining the stability of the soil, the main factor of the coupling of the adhesion between the soil particles is substantiated. The suffusion process occurring as a result of moistening and salinization of the soil of the shores of an open reservoir is estimated and analyzed, decreasing of stagnation coefficient in a result of ground absorption process. Analyzing soil samples of various salinities under laboratory conditions, soil adhesion was evaluated. Based on the studies, recommendations are given for the restoration of the shores of open drainages. The given recommendations are based on increasing the drainage's resilience and preventing deformation processes from constructing the drainage in slopes.

Identifying plant diseases using deep transfer learning and enhanced lightweight network

Plant diseases can cause significant reductions in both the quality and quantity of agricultural products, and they have a disastrous impact on the safety of food production. In severe cases, plant diseases may even lead to no grain harvest completely. Therefore, seeking fast, automatic, less expensive and accurate methods to detect plant diseases is of great realistic significance. In this paper, we studied the transfer learning for the deep CNNs and modified the network structure to enhance the learning ability of the tiny lesion symptoms. The pre-trained MobileNet-V2 was extended with the classification activation map (CAM), which was used for visualization as well as plant lesion positioning, and both were selected in our approach. Particularly, the transfer learning was performed twice in model training: the first phase only inferred the weights from scratch for new extended layers while the bottom convolution layers were frozen with the parameters trained from ImageNet; the second phase retrained the weights using the target dataset by loading the model trained in the first phase. Then, the yielded optimum model was used for identifying plant diseases. Experimental results demonstrate the validity of the proposed approach. It achieves an average recognition accuracy of 99.85% on the public dataset. Even under multiple classes and complex background conditions, the average accuracy reaches 99.11% on the collected plant disease images. Thus, the proposed approach efficiently accomplished plant disease identification and presented a superior performance relative to other state-of-the-art methods.

SISTEM INFORMASI GEOGRAFIS BERBASIS WEB DAERAH IRIGASI BATANG ALAI SELATAN KABUPATEN HULU SUNGAI TENGAH

Jaringan irigiasi merupakan salah satu prasarana yang dibutuhkan dalam upaya meningkatkan kualitas dan kuantitas produksi pertanian. Hal ini tidak lepas dari kondisi saluran irigasi yang baik dan pemeliharaan yang benar . Dalam proses pembangunan daerah irigasi Batang Alai Selatan yang merupakan tujuan dibangunnya sistem informasi jaringan irigasi D.I Batang Alai Selatan Dalam bentuk website yang dapat diakses secara online. Penelitian ini menggunakan metode pengumpulan 2 data, yaitu : primer yang didapatkan dari hasil penelusuran jaringan irigasi pengambilan titik koordinat bangunan dan saluran menggunakan GPS Map dilapangan secara langsung, dan data sekunder didapat dari data yang bersumber dari dokumen resmi dari instansi terkait. Dari 2 data tersebut maka penulis dapat membangun sebuah sistem informasi D.I Batang Alai Berbasis Online. Hasil penelitian berupa Sistem Informasi Geografis berbasis Web D.I Batang Alai Selatan yang terdiri dari (i) data progress pekerjaan, (ii) data inventaris (iii) data realisasi dan (iv) peta daerah irigasi yang dapat diakses secara online guna memudahkan pengguna melihat posisi jaringan irigasi secara detail. Website ini dirancang menggunakan tampilan yang dinamis menggunakan bahasa pemograman PHP dan MySQL sebagai pengolahan database serta mengunakan sistem online dengan syarat perangkat harus terhubung dengan jaringan internet.
 
 Kata Kunci : Irigasi, Batang Alai, PHP, MySQL,Webgis

Prototype and Method for Crops Analysis in the Visible and Infrared Spectrum from a Multirotor Air Vehicle

Plant health has a direct impact on the quality and quantity of agricultural products. Due to this fact, farmers must monitor crop conditions frequently. However, the current tools for achieving this are complex and inaccessible. Therefore, this article proposes a method for the characterization of crops that allows to monitor the plants using photographs in the visible and infrared spectrum acquired from a multi-rotor air vehicle, using low-cost cameras and free use tools for designing a prototype of processing information. The characterization is performed by identifying the normalized difference vegetation index (NDVI) in the photographic mosaics of the crops. This index provides information about plant health: Consequently, it is calculated and represented on a NDVI map, where the status of a crop is analyzed. The highest values of NDVI represent healthy plants, and the lowest do so for plants with problems, water, or others. The proposed ethod allows the monitoring of crops in a temporary and spatial form, letting a producer to adopt measures that help the optimization of resources.

Using deep transfer learning for image-based plant disease identification

Plant diseases have a disastrous impact on the safety of food production, and they can cause a significant reduction in both the quality and quantity of agricultural products. In severe cases, plant diseases may even cause no grain harvest entirely. Thus, the automatic identification and diagnosis of plant diseases are highly desired in the field of agricultural information. Many methods have been proposed for solving this task, where deep learning is becoming the preferred method due to the impressive performance. In this work, we study transfer learning of the deep convolutional neural networks for the identification of plant leaf diseases and consider using the pre-trained model learned from the typical massive datasets, and then transfer to the specific task trained by our own data. The VGGNet pre-trained on ImageNet and Inception module are selected in our approach. Instead of starting the training from scratch by randomly initializing the weights, we initialize the weights using the pre-trained networks on the large labeled dataset, ImageNet. The proposed approach presents a substantial performance improvement with respect to other state-of-the-art methods; it achieves a validation accuracy of no less than 91.83% on the public dataset. Even under complex background conditions, the average accuracy of the proposed approach reaches 92.00% for the class prediction of rice plant images. Experimental results demonstrate the validity of the proposed approach, and it is accomplished efficiently for plant disease detection.