Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning (original) (raw)
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Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021
In the face of a focus on development, a farming residue a most powerful sector of an Indian financial management system both regarding the benefaction to gross domestic product (GDP) and also the source of utilization to the billions of people across the country. Agriculture is an agribusiness of the farmer that take part in the Indian financial management system. More than 75 percent of farm households depend on agriculture for their livelihood. But pest infestation in crops is a serious problem that slows down the growth of agricultural production. Crop disease identification in the agricultural sector is essential to deal with such problems. This present paper provides a technical solution to solve the type of issue in which the CNN algorithm is used to diagnose crop diseases and involves automatic pesticide spraying to spray pesticides on the affected crops locally. The system is based on pesticide sprays. The design deals with three modules image acquisition, image pre-process...
Artificial Intelligence in Agriculture for Application of Pesticides and Herbicides
The agricultural sector is vital to the economy. Agricultural automation is a subject that is getting more and more attention and interest around the world. With the growing number of people, the need for both food and work is rising quickly. The farmers' tried-and-true ways of doing things didn't meet these standards, though. So, high-tech, machine-based methods have come into being. Not only did these new eISSN1303-5150 www.neuroquantology.com ways of doing things meet the need for food, but they also gave jobs to billions of people. AI has changed the way farming is done in a big way. The effects of climate change, population growth, an unstable job market, and food shortages have been lessened by this new idea. The main goal of this study is to take a look at all of the different ways AI can be used in farming, such as putting cameras and other tools on robots and drones to help with irrigation, weeding, and spraying. These innovations cut down on the wasteful use of water, pesticides, and herbicides. They also protect the soil's nutrition, help people work more efficiently, and increase both output and quality. This study is a summary of what many researchers have found about the state of automation in agriculture, especially in terms of how robots and drones can be used to get rid of weeds. In addition to the different soil water sensing systems, there are also two automatic ways to get rid of weeds. This study talks about drones and the many ways they can be used to spray crops and keep an eye on them. It also talks about how they can be used.
Artificial Intelligence and Machine Learning Operated Pesticide Sprayer
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Robotics has influenced all modern day infrastructures and developments, and is crucial to find its way through traditional agricultural practices that are still mostly done by manpower and human interference. Pesticides being chemical agents, if sprayed excessively on agricultural/organic matter may highly influence its health, and also cause soil degradation to a very high extent leading to degradation of farm fertility, as most of the farmers being un aware of the damages that such excessive insecticide sprays can cause to land and plant health. Thus, our prime motive is to develop a robot governed by Artificial Intelligence and Machine Learning algorithm dedicating itself to spray pesticides only where it is essential, by incorporating machine vision. Thus, analyzing plants, monitoring their health status, and spraying pesticides if and only if plant is prone to infestation or attack.
RECOMMENDATION OF CROP AND PESTICIDES USING MACHINE LEARNING
The major resource for improving the economy of India is agriculture. From past farmers followed ancestral faming pattern and regularities within it. A single farmer cannot take action upon improving the crop yield of a nation and does not have enough potential to maximize the crop yield by adopting technical norms within plant growth and improving the yield in a large quantity. Severe change in climatic condition and several other pesticides attack cause shorting of crop yield and also led to food shortage. A simple misguided decision in farming can affect a farmer severe. In recent, there is lot of techniques applied by researchers and those techniques are available to raise the quantity of yield. This in turn changed traditional farming approach and introduced precision farming. Recently data mining performs vital role in identifying plant disease and providing solution prescribing pesticides to plant disease. But this study extends the application of data mining in agriculture to a greater extent. The cultivation of precious crop at right time is the major issues faced by farmer. This study proposes machine learning (ML) approach to resolve it and makes the farmer to choose right crop based on the nutrition content and quality of soil. The machine learning algorithms chosen for this study are Random forest, decision tree and K-nearest neighboring. Some of the factors mainly considered for recommendation of plant are humidity, rainfall, pH value, soil moisture. The recommended technique makes farmer to take decision on improving the crop yield; recommending crops as per climatic condition and quality of land.
PEST MANAGEMENT USING MACHINE LEARNING ALGORITHMS: A REVIEW
Agriculture is a unique business crop production, which is dependent on many climate and economy factors, the major occupation of Indians is farming where in about 70% of the population depends on agriculture. Farmers have wide range of diversity to select suitable Fruit and Vegetable crops. However, the cultivation of these crops for optimum yield and quality produce is highly technical. The crop production has reduced due to various factors like pest attack, diseases and climatic conditions. Crop protection is the science and practice of managing pests, plant diseases and other pest organisms that damage agricultural crops. Machine learning is a looming field of computer science which can be applied to the farming sector quite effectively. It can facilitate the up-gradation of conventional farming techniques in the most cost-friendly approach. This paper reviews on how different machine learning algorithms are useful in pest management of various crops.
Using AI to Recommend Pesticides for Effective Management of Multiple Plant Diseases
IRJET, 2023
Trees, Plants and Crops are one of the principal sources of food for humans as well as other animals. They are crucial for our continuance. Similar to us they are also living organisms. Once in a while we get afflicted by diverse diseases. Like us, plants are also affected by various types of illness. Plants that are infected by disease have results on their health which have severe consequences like less food production. Most plant ailments are contagious which spread rapidly all over the whole crop. Prior prevention and ceasing of disease is a necessity step to stop further harm and proper crop production. Usually, farmers or professionals keep a close eye on the plants in order to discover and identify diseases. However, this procedure is frequently time-consuming, costly, and imprecise. We need to ameliorate and quicken the process of disease perception and its diagnosis. The main aim of this research paper is to demonstrate a Disease Recognition System that is supported by providing solutions with Fertilizer Recommendation to make plant disease spotting easier and briskly. In this research paper we are providing methodology to make use of Computer Vision with a Machine Learning Model (Convolution Neural Network) to make an effective system for plant disease detection. CNN is a form of artificial neural network that is specifically intended to process pixel input and it is used in image recognition. Overall, we are intended to provide a method using machine learning to detect the disease present in plants on a colossal scale.
ITM Web of Conferences
Pests are divided into two as herbal and animal pests in agriculture, and detection and use of minimum pesticides are quite challenging task. Last three decades, researchers have been improving their studies on these manners. Therefore, effective, efficient, and as well as intelligent systems are designed and modelled. In this paper, an intelligent classification system is designed for detecting pests as herbal or animal to use of proper pesticides accordingly. The designed system suggests two main stages. Firstly, images are processed using different image processing techniques that images have specific distinguishing geometric patterns. The second stage is neural network phase for classification. A backpropagation neural network is used for training and testing with processed images. System is tested, and experiment results show efficiency and effective classification rate. Autonomy and time efficiency within the pesticide usage are also discussed.
Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence
Applied Sciences, 2021
Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named “rover”), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover ...
CROP DAMAGE DETECTION DUE TO EXCESS USE OF PESTICIDES AND FERTILIZERS USING MACHINE LEARNING
The paper addresses the critical issue of crop damage due to excessive use of pesticides and fertilizers in agriculture, which leads to financial losses and environmental degradation. To mitigate this, the study proposes a novel solution utilizing machine learning techniques. It emphasizes the integration of advanced pre-processing methods like label encoding, standard scalar normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The core of the solution involves the application of the Extra Trees Classifier and AdaBoost methods, forming an ensemble approach for improved prediction accuracy. This combination is unique in its incorporation of extra randomization and adaptive boosting, enhancing the model's robustness and efficiency, especially with large and complex datasets. The paper's novelty lies in its approach to integrating these advanced machine learning techniques for early detection and accurate assessment of agricultural damage, contributing significantly to sustainable agricultural practices.