RECOMMENDATION OF CROP AND PESTICIDES USING MACHINE LEARNING (original) (raw)
Related papers
Crop Recommendation System to Maximize Crop Yield in Ramtek region using Machine Learning
International Journal of Scientific Research in Science and Technology, 2019
In Indian economy and employment agriculture plays major role. The most common problem faced by the Indian farmers is they do not opt crop based on the necessity of soil, as a result they face serious setback in productivity. This problem can be addressed through precision agriculture. This method takes three parameters into consideration, viz: soil characteristics, soil types and crop yield data collection based on these parameters suggesting the farmer suitable crop to be cultivated. Precision agriculture helps in reduction of non suitable crop which indeed increases productivity, apart from the following advantages like efficacy in input as well as output and better decision making for farming. This method gives solutions like proposing a recommendation system through an ensemble model with majority voting techniques using random tree, CHAID, K _ Nearest Neighbour and Naive Bayes as learner to recommend suitable crop based on soil parameters with high specific accuracy and efficiency. The classified image generated by these techniques consists of ground truth statistical data and parameters of it are weather, crop yield, state and district wise crops to predict the yield of a particular crop under particular weather condition.
2022
With the use of an intelligent system called Crop Recommender, this project attempts to help Indian farmers choose the best crop to grow based on the qualities of the soil, as well as external parameters like temperature and rainfall. Indian economy is significantly influenced by the agricultural sector. The majority of Indians rely on agriculture for their living, either overtly or covertly. Thus, it can be said with certainty that agriculture is important to the nation. The majority of Indian farmers think that when choosing a crop to plant in a given season, they should rely on their instincts or simply they use their traditional methods which they have been using since old era. Instead of understanding it completely the crop productivity, is contingent on the current weather and soil conditions, they are more at ease merely adhering to established agricultural practices and standards. A single poor choice made by the farmer could result in unintended loss for both himself and the local agricultural sector. As the whole lateral system based on the agricultural industry. The machine learning algorithm can be used to solve this issue. The implementation of a recommendation system uses decision trees. The main objectives of this system are to advise farmers on which crops to plant which is suitable to its soil and seasonal rainfall.
Crop Recommendation System for Madhya Pradesh Districts using Machine Learning
Zenodo (CERN European Organization for Nuclear Research), 2023
Recommendation systems have become increasingly common in recent years. The use of recommender systems has become common in an assortment of commercial applications. There are software tools and techniques that provide suggestions for items of use to the user called recommender systems. Recommender systems help users to make various decisions such as what item to buy, what music to listen to, which book to read, etc. But in the field of agriculture, the selection of crop and cropping techniques has a significant impact on the productivity and financial success of farmers. So many parameters including rainfall, soil properties, crop rotation, land preparation, and uncontrollable elements like weather, have an impact on crop recommendations are involved with uncertainty a good recommender system is required. It is unfortunate that there is no universal system to assist farmers in agriculture. Most Indian farmers cultivate at their own discretion and follow the pattern and norms of ancestral farming. Due to a lack of adequate technical knowledge, they do not get enough production and profit. By using recommendations that are appropriate for them, the proposed agricultural recommender system will assist the farmers in minimising their losses and maximizing their profits. Based on the necessary parameters, this system helps the farmers in selecting a suitable crop for farming.
Machine Learning based Crop Recommendation System for Local Farmers of Pakistan
Revista Gestão Inovação e Tecnologias, 2021
Farming is one of the most fundamental and generally rehearsed work in Pakistan and it plays an imperative part in fostering the country. In Pakistan, the most part of the land is used for agriculture cultivation to meet the desires of nearby people and export want as properly. Therefore, the need of increasing crop production is the significant challenge for farmers. Crop cultivation anywhere in the world depends on the climate so called seasons and soil properties, however, the enhancing the production of crops depend on various factors like mainly on temperature. In order to address the issue of increasing crop production for Pakistan, a crop recommendation system is proposed in this work. In this work, idea of ideal harvest prior to planting it, it would be of extraordinary assistance to the farmers and others required to settle on fitting choices on upgrading the creation of yields for neighborhood utilization needs and may prompt the capacity and expanded fare choice for busin...
Review of Machine Learning Techniques for Crop Recommendation System
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The Indian population is highly dependent on agriculture for vegetables, fruits, grains, natural textile fibres like cotton, jute, and many more. Also, the agricultural sector plays a vital role in the economic growth of the country. The agriculture sector is contributing around 19.9 percent since 2020-2021. As a result, agricultural production in India has a significant impact on employment. The soil in India has been in use for thousands of years, resulting in depletion and exhaustion of nutrients and minerals, which leads to a reduction of crop yield. Also, there is a lack of modern applications, which causes a need for precision agriculture. Precision Agriculture, also known as Satellite farming is a series of strategies and tools to manage farms based on observing, measuring, and responding to crop variability both within and between fields. One of the main applications of precision agriculture is the recommendation of accurate crops. It helps in increasing crop yield and gaining profits. This paper aims to review and analyse the implementation and performance of various methodologies on crop recommendation systems.
Crop Recommendation for Maximizing Crop Yield Using Random Forest
The agriculture sector is a vital part of India's economy. About 54.6% of the workers are employed in agricultural and allied activities, and 18.8% of the India's gross value added (GVA) is generated by these activities. One of the common problems faced by Indian young farmers is choosing the right crop according to the soil conditions. This has led to a significant setback in productivity in agriculture. This study will help the farmers to determine which crop will be suitable to grow on their soil; thus, the prime motive of this study is to create economic welfare to farmers. The dataset used in this study was from different Indian government website and is publicly available. Based on seven different attributes, i.e., nitrogen, phosphorous, potassium, temperature, relative humidity, pH value, and rainfall, a crop is recommended to grow. Four different machine learning algorithms, i.e., Naive Bayes, decision tree, logistic regression, and random forest were used on the data. Random forest's testing accuracy, i.e., R 2 was about 99%, and hence, it was used to develop and deploy a cloud-based app which recommends a particular crop to be grown in a particular soil.
Recommendation of Agricultural Crop Based on Productivity and Season Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Agriculture gave birth to civilization. India is an agrarian country and its economy largely based upon crop productivity. Thus agriculture is that the backbone of all business in India. Now India stands in second rank in worldwide in farm production. India is an agricultural country but remains using traditional ways of recommendations for agricultural purpose. Presently, recommendations for farmers are supported one to at least one interaction between farmers and therefore the experts and different experts have different recommendations.
Crop Prediction and Fertilizer Recommendation Using Machine Learning
India's global economy is critically dependent on agriculture, which also accounts for a sizeable portion of GDP. As the world's population grows, it is essential to maintain food security, which is made possible and managed by the country's agricultural output.Planning for agriculture is important for agro-based nations' economic development and food security. Agriculture continues to face a number of issues. The choice of the crop to be farmed presents many challenges for farmers. Farmers face a credit collapse if they grow the incorrect crop. In order to feed the world's population, agriculture's productivity must undergo massive expansion. The most widely recognised technological innovation is machine learning; however, there are several more. By investigating the composition and other traits of the soil, it is envisioned that the right crop and fertiliser would be forecast as part of this proposed work. Crop prediction assists farmers in selecting the ideal crop for planting in order to enhance output and profitability.
Machine Learning Approach for Crop and Fertilizer Recommendation
Background: India's agricultural expertise as the world's largest producer covers a range of areas, including dry fruits, textile raw materials, pulses, farmed fish, and more. However, there is a challenge in farming methods, where farmers often rely heavily on fertilizers and grow the same crop season after season without much understanding. Objectives: To overcome this challenge, a forward-thinking initiative has incorporated machine learning to revolutionize farming practices. Methods: This transformative step involves a customized recommendation system that utilizes machine learning algorithms to assist farmers in selecting crops and applying precise amounts of fertilizer based on their specific soil and weather conditions. Statistical Analysis: The ultimate goal is to move from single-crop cultivation and enable farmers to diversify their offerings throughout different seasons. The benefits are numerous. Applications: This approach holds the promise of increased profitability through diversified crops and optimized fertilizer usage, while also promoting sustainability. By encouraging crop rotation and the informed use of fertilizers, this initiative aims to reduce soil pollution and contribute to the long-term well-being of ecosystems. Improvements: Essentially, deploying this machine learning-based model represents a leap in modernizing and optimizing the Indian sector while empowering farmers toward a more resilient, sustainable, and economically viable future.
Crop Recommendation Using Machine Learning Algorithm
Agriculture is extremely important to India's economy and employment. The most common issue faced by Indian farmers is that farmers do not select the appropriate crop for their soil. As a result, productivity is harmed. Agriculture is the main source of income and the backbone of our economy. The poor crop selection has reduced crop production and food shortages across the country which resulted in an increase in farmer suicide. Farmers' problems have been handled by recommendation of suitable crop before sowing. To overcome these issues it is necessary to analyze the soil parameters. This proposed work presents the SVM algorithm based crop recommendation system for the formers. In this work, it is necessary to analyze the profit of the particular crop, which eliminates the loss for the farmers and increase the productivity. SVM algorithm is used for classification to classify the different parameters of the soil and predict the most suitable crop.The proposed algorithm is simulated in anaconda navigator to analyze the soil parameters and recommend a suitable crop. The SVM algorithm is considered for classification. To test the effectiveness of the proposed algorithm accuracy and confusion matrix are computed.