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Agricultural product costs play a significant part in the horticultural market. In India, vegetables, for example, tomatoes have the biggest supply and price variances among farming items. As tomatoes are grown around the year, outdoor and indoor, their yields change because of various factors, it is hard to settle tomatoes' inventory and costs. Although the Government puts numerous efforts to balance out the supply and costs of vegetables, continuous meteorological changes have prompted unstable supply and price fluctuations of vegetables. Accordingly, the right anticipating of vegetable costs is a significant issue. To oblige these, in this paper, an attempt has been made to dissect the costs and yield of tomatoes in India by utilizing a Machine Learning approach. This will unquestionably help the farmers and the Government if the anticipated costs are getting higher in the forthcoming months, then appropriate strategies can be made to diminish the costs of tomatoes. [ABSTRACT FROM AUTHOR]

Effects of Bacillus thuringiensis (Bt) cotton are at the forefront of an intense debate on the benefits of genetically modified (GM) crops among smallholder farmers in developing countries. Existing studies fail to control for confounders, selection bias, or cultivation bias from preferential treatment in the initial adoption phase. Addressing these concerns in this paper, I examine the impact of Bt cotton employing an unbalanced panel fixed-effects model of a crop yield and profit function on newly collected plot-level data in the most recent decade. Results show that Bt cotton yields have stagnated, have a null effect on profits, and have become more sensitive to pest pressure in the most recent decade. Though many studies have demonstrated higher crop yield and profit gains in the first decade of Bt cotton adoption that raised the average returns to the technology, the second decade shows convergence in benefits, which raises obvious questions about the prospect of GM technology. Since Bt cotton is the only GM crop technology widely adopted by smallholder farmers, the findings of this paper contribute to the broader public debate on the future of agricultural biotechnology. [ABSTRACT FROM AUTHOR]

Background and Aims: The accessibility and relatively low cost of nitrogen (N) fertilizers have been a gift to humankind and made it possible to feed the exponentially growing world population. The excessive use of N, in combination with a poor N use efficiency (NUE) in crop production, is associated with severe environmental impacts, and the scientific community has repeatedly warned that the safe operating space for N within the planetary boundaries has been exceeded. In the light of these considerations, a global player and India's largest fertilizer manufacturer, the Indian Farmers Fertilizer Cooperative (IFFCO), has recently developed and patented a nanotechnology-based fertilizer marketed as Nano Urea (liquid). Strikingly, the producers state that it is now possible to replace a 45 kg bag of conventional urea, containing 21 kg N, by foliar application of just 20 g N in the form of nano urea (NU). If so, N from this novel high-tech fertilizer product should be able to increase the NUE of crops more than 1000 times compared to conventional urea. This year, the Indian government and IFFCO announced plans to massively boost production by building 10 new factories, with an annual production capacity of 440 million bottles NU by 2025 and to expand export of the product to another 25 countries, mainly situated in Asia, Africa and South-America. In the marketing of NU, IFFCO states that there is scientific evidence for distinct beneficial properties in terms of higher crop yields and reduced negative environmental impacts. Similar ambitions have recently been presented for Nano Zinc, Nano Copper, and Nano Di-Ammonium-Phosphate, a product that entered the market in March 2023. Methods: In this Opinion Paper, we compare the claims made by IFFCO scientists to the existing scientific evidence in the field of foliar nanofertilizers. Results: We observe that NU is a poorly described product with no scientifically proven effects. The product is marketed with misleading and wrong statements about its fertilizer efficiency, the underlying plant uptake pathways, and the environmental friendliness. Conclusion: The expectations raised by IFFCO are far from reality and may lead to large-scale yield losses with serious consequences for food security and the livelihood of farmers. At the same time, the confidence in innovative sustainable products as well as the science behind them may be threatened. Based on the IFFCO case, and considering the booming emergence of novel nano based fertilizers appearing all over the world these years, it is clear that much more priority should be given to scientifically prove their efficacy and mode of action, before they are launched on the markets. [ABSTRACT FROM AUTHOR]

About 70% of India's economy is involved in the agriculture sector to live their lives and contributed to the GDP of the country. The Crop yield information along with the environmental change estimate will be useful for the agriculturalist to decide on price policies prior to harvesting the food source. It establishes a requirement for the prediction model, which precisely determines the harvest conditions, crop varieties, and agricultural yield. In literature, numerous crop prediction methods were devised to estimate crop production in the agricultural field & each technique has its potential in terms of yield forecasting. This review article provides a detailed analysis of the utilized approaches in the literature for the prediction of crop production as well as a discussion on the identification of concerns related to the yield gaps of crops. The discussed approaches were classified based on the application of different strategies, such as Machine learning methods, Deep learning methods, Data mining techniques, vegetative indices, fuzzy logic, and hybrid methods. The study was analyzed based on performance metrics, year of publication, datasets employed, software used for experimentation, and performance attained using various methods and highlights the research gaps of the respective method along with the future direction. [ABSTRACT FROM AUTHOR]

To meet the demand of the growing population, there exists pressure on food production. In this context, appropriate prediction of crop yield helps in agricultural production planning. Given the inability of the traditional linear models to provide satisfactory prediction performance, there is a need to develop a crop yield prediction model that is simple in complexity, accurate in prediction, and less time-consuming during training and validation phases. Keeping these objectives in view, the present paper focuses on building an adaptive, low complexity, and accurate nonlinear model for the prediction of crop yield. A time series dataset for the period 1991–2012 of Karnataka, a southwestern state of India, is used for yield prediction. An empirical nonlinear relation between crop yield and the four independent attributes has been obtained from the proposed ANN model. The independent attributes employed are total rainfall, the cumulative distribution of temperature, the proportion of irrigated land, and the average amount of fertilizer used. It is demonstrated that the developed model exhibits better prediction accuracy, less root mean square error in the range of 0.07–0.14, less mean square error in the range of 0.01–0.04, and mean absolute error in the range of 0.07–0.15 compared to its corresponding linear regression model. It is recommended that the proposed ANN model can also be applied to predict other agricultural products of the same or other geographical regions of the globe. [ABSTRACT FROM AUTHOR]

Declining per capita land, water and other resources triggered by changing climate and increasing population need resource conservation technologies (RCTs) for agricultural sustainability. This paper has presented findings from recent research on resource conservation technologies that have been attempted in different regions of India. The superiority of resource conservation technologies (RCTs) such as laser land leveling, zero tillage, bed planting, direct seeded rice, leaf colour chart, green seeker and crop diversification over conventional methods of cultivation practices is evaluated in terms of saving of energy, water and labour, increase in crop yield, water productivity, nutrient-use efficiency, net returns and making agriculture climate smart. Zero tillage with residue retention increase soil organic carbon sequestration, conserve soil moisture and lower soil temperature thus making agriculture climate smart. Direct seeded rice is solution for labour scarcity, excessive water and fuel use in transplanted rice. Use of leaf colour chart saves nitrogenous fertilizers. Laser land leveling and bed planting practices increase water productivity and crop yield over conventional practices. But the machinery required for adoption of RCTs are heavy and costly, so the study has suggested the use of machines on co-operative basis and also the wider scale testing of new technologies under diverse production systems. [ABSTRACT FROM AUTHOR]

Using data from a survey of farm households in the Indian state of Uttar Pradesh and employing a sequential logit model, this paper has investigated the impact of farmers' participation in the Direct Benefit Transfer (DBT) subsidy scheme for micro-irrigation on crop performance. The findings show that over 91% of the farmers were aware of the scheme, 61% of them registered as potential beneficiaries and two-thirds of them actually benefitted from it. Registration is the critical transition stage for participation in the DBT scheme. Our findings show that conditional upon the ownership of tube-wells and water pumping devices, several personal and household-level factors such as age, education, access to information and membership of village-level organizations distinguish the beneficiaries of DBT from the non-beneficiaries. There appears no selection bias in the scheme as neither the farm size nor the social status of potential beneficiaries appeared significant in the sequential selection process. Further, the findings show that controlling for the farm and farmer characteristics, the DBT scheme has a positive and statistically significant impact on crop yield as well as net returns, but more on the latter implying its role in reduction in the cost of production. These findings imply the need to expand DBT scheme for its greater impact on farm income and inclusiveness. [ABSTRACT FROM AUTHOR]

Agriculture is the main source of income, food, employment, and livelihood for most rural people in India. Several crops can be destroyed yearly due to a lack of technical skills and changing weather patterns such as rainfall, temperature, and other atmospheric parameters that play an enormous role in determining crop yield and profit. Therefore, selecting a suitable crop to increase crop yield is an essential aspect of improving real-life farming scenarios. Anticipating crop yield is one of the major concerns in agriculture and plays a critical role in global, regional, and field decision-making. Crop yield forecasting is based on crop parameters and meteorological, atmospheric, and soil conditions. This paper introduces a crop recommendation and yield prediction system using a Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends crops and forecasts crop yield accurately and promptly. The proposed model used a Probabilistic Neural Network (PNN) for crop recommendation and the Extreme Learning Machine (ELM) method for the crop yield forecasting process. The HMFO algorithm was used to improve the forecasting rate of the ELM approach. A wide-ranging simulation analysis was carried out to evaluate the HMFO-ML model, showing its advantages over other models, as it exhibited a maximum R² score of 98.82% and an accuracy of 99.67%. [ABSTRACT FROM AUTHOR]

Agriculture plays a vital role in the development of India, about 70% of the population is directly or indirectly dependent on agriculture, agriculture contributes 20% of India's GDP. Smart Agriculture is an application of IoT(Internet of Things) in agricultural practices. Use of the technology will be very helpful and beneficial for farmers and thereby for the country. This project will automate various aspects of the agricultural domain and thus help to reduce the burden of farmers. In the Smart Agriculture project, there has been a use of various sensors, NodeMCU, LCD and many other peripherals used in IoT. These devices help to detect various parameters involved in the agricultural field. IoT modernization helps in gathering information on circumstances like climate, dampness, temperature and fruitfulness of soil, by detecting these readings farmers can change their farming practices accordingly, to get maximum yield and a good crop. As the population is predicted to reach 10 billion people in 2060, and as a result of the population's rapid rise, there is a sudden surge in demand for food. Unfortunately, this increase in demand is only tangentially related to population growth. That is why there is a need for development of a system that will sustain this rising demand. [ABSTRACT FROM AUTHOR]

Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%. [ABSTRACT FROM AUTHOR]

Background: Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer’s experience on a particular type of field and crop. Predicting the crop is done by the farmer’s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities. Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield. Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool. [ABSTRACT FROM AUTHOR]

India's economy is rooted in agriculture, and the majority of the farmers, being smallholders, are highly exposed to the adversities associated with climate change. It has been argued by several researchers that perceiving climate change is a pre-condition for the adoption of adaptation strategies to reduce vulnerabilities. In this regard, studies are limited to explore the question 'is the perceptions of changing climate primarily motivate the smallholders to take adaptation actions?' In this nexus, the present paper seeks to understand the linkages between smallholders' climate change perception and the changes in their farming practices. The study was conducted in the seven villages of Sub-Himalayan West Bengal, India, using participatory rural appraisal (PRA) tools. Key results show, (1) respondents perceived increased warming and decreased precipitation in accordance with the scientific analysis; (2) abundance of groundwater, misconceptions and lack of awareness shaped respondents' agricultural risk perception, and despite decreasing precipitation, water management appeared less risky (3) incorporation of irrigation, diversification towards high valued and high yielding crops, as well as farm mechanizations were the major changes in the farming practices; and, (4) except water management, rest of the changes were not necessarily made to adapt with climate change instead to increase crop yield and profit. It is noticed that the changes in farming practices perhaps made the farmers unconsciously adapted to climate change. Still, a prolonged continuation of some of those practices may lead to maladaptation. In this regard, we have given direction towards necessary policy formulation and future research to achieve sustainability. [ABSTRACT FROM AUTHOR]

Farmers submit claims to insurance providers when affected by sowing/planting risk, standing crop risk, post-harvest risk, and localized calamities risk. Decision making for settlement of claims submitted by farmers has been observed to comprise of type-I and type-II errors. The existence of these errors reduces confidence on agri-insurance providers and government in general as it fails to serve the needy farmers (type-I error) and sometimes serve the ineligible farmers (type-II error). The gaps in currently used underlying data, methods and timelines including anomalies in locational data used in crop sampling, inclusion of invalid data points in computation, estimation of crop yield, and determination of the total sown area create barriers in executing the indemnity payments for small and marginal farmers in India. In this paper, we present a satellite big data analytics based case study in a region in India and explain how the anomalies in the legacy processes were addressed to minimize type-I and type-II errors and thereby make ethical decisions while approving farmer claims. Our study demonstrates what big data analytics can offer to increase the ethicality of the decisions and the confidence at which the decision is made, especially when the beneficiaries of the decision are poor and powerless. [ABSTRACT FROM AUTHOR]

Climate change is a major concern the world over more so for a predominantly agrarian country like India. In this paper we analyze the time horizon dynamics of crop and climate variables at the regional level in India. We also analyze the co-movements of crop yields with temperature and rainfall to observe the coherence across heterogenous time horizons. We employ Bai-Perron structural break and Continuous wavelet transform methods on yearly data of seven crop yields and climate variables. Observed variables are analyzed from 1956 to 2010 for the un-divided state of Andhra Pradesh, India. Breakpoint analysis shows an increase of around 1.0° temperature with two observed break points. Rainfall depicts no systematic change with fluctuations being largely random. The framework of wavelets-based time–frequency analysis employed in this study captures climate and crop dynamics at heterogeneous time horizons, allowing one to study the impact of climate and crop yields at both short and longer time-horizons. Wavelet based coherence analysis exhibited significant co-movement between climatic and crop variables. Given shifts in climate patterns and subsequent shifts in co-movements across time horizons at the regional level, policy makers and crop scientists should design time specific and locally viable adaption and mitigation policies to tackle the impact of climate change on crops and livelihoods. [ABSTRACT FROM AUTHOR]

Crop yield forecasting is becoming more essential in the current scenario when food security must be assured, despite the problems posed by an increasingly globalized community and other environmental challenges such as climate change and natural disasters. Several factors influence crop yield prediction, which has complex non-linear relationships. Hence, to study these relationships, machine learning methodologies have been increasingly adopted from conventional statistical methods. With wheat being a primary and staple food crop in the Indian community, ensuring the country's food security is crucial. In this paper, we study the prediction of wheat yield for India overall and the top wheat-producing states with a comparison. To accomplish this, we use Multivariate Adaptive Regression Splines (MARS) after extracting the main features by Principal Component Analysis (PCA) considering the parameters such as area under cultivation and production for the years 1962–2018. The performance is evaluated by error analyses such as RMSE, MAE, and R2. The best-fitted MARS model is chosen using cross-validation and user-defined parameter optimization. We find that the MARS model is well suited to India as a whole and other top wheat-producing states. A comparative result is obtained on yield prediction between India overall and other states, wherein the state of Rajasthan has a better model than other major wheat-producing states. This research will emphasize the importance of improved government decision-making as well as increased knowledge and robust forecasting among Indian farmers in various states. [ABSTRACT FROM AUTHOR]

Cotton is one of the most important commercial crops in India. Cotton growth is very slow in the initial stages of its life cycle and row spacing is wider which provides ample space for the growth of different categories of weeds and thus become a source of competition for water as well as nutrients, thereby impeding its growth. Weeds are major constraints that reduce the crop yields since they compete with crop for the nutrients, moisture, light and space. The integration of different weed management practices would be a viable option for broad spectrum weed control and enhancement of cotton productivity. Thus, weed management has several aspects such as physical, mechanical, cultural, chemical and integrated weed management methods. A brief review of research accomplishment made at various places on different fields related to this investigation is reviewed in this paper. [ABSTRACT FROM AUTHOR]

Bihar, India's poorest state, witnessed impressive yield growth in its three principal crops during the last decade or so. This paper examines whether a reduction in regional yield disparities accompanied these state-level productivity gains. The convergence test allows the district-specific element of productivity to vary with time, thus permitting divergence in interim phases. District rice yields converged towards a common growth path. The results for wheat and maize are not as conclusive. However, there is strong evidence of divergence for each crop in recent years. This divergence, nonetheless, happens in the presence of club convergence. The club classification transcends agro-climatic boundaries, indicating a role for policy to aid growth in the lagging districts. The shifts in credit allocation over the years do not appear to be driving the yield divergence, highlighting the limitations of a macro credit-driven policy. [ABSTRACT FROM AUTHOR]

Rice is an important food crop extensively grown in India. It is mainly grown as a wetland crop by transplanting seedlings into puddled fields. Conventional transplanting is the most common practice of rice cultivation in south India. Several factors are responsible for reducing the yield of transplanted rice. One of the cause for yield reduction is weed infestation. Weeds by the virtue of their high adaptability and faster growth dominate the crop habitat and reduce the yield potential of the crop. Normally the loss in yield ranges between 15-20 per cent, yet in severe cases the yield losses can be more than 50 per cent, depending upon the species and intensity of weeds. In this paper, we will summarize and review possible effect of weeds on transplanted rice, weed species, different methods of weed control, labour saving and economics of rice. [ABSTRACT FROM AUTHOR]

Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work. [ABSTRACT FROM AUTHOR]

Weather factors, like other inputs such as land, labor, seeds, irrigation, fertilizer, and pesticides are also direct inputs in crop production. In a state of agriculture where the adoption and diffusion of modern technologies is very low or almost nil, weather factors count more than other inputs because of their direct and indirect effects. Thus, the link between weather and crop yield will have implications on food supply and crop forecasting and management policies. It is of immense importance to the policymakers, agricultural scientists, agricultural economists, and meteorologists to understand this relationship. The methodology for studying this relationship has undergone many improvements over time. This paper attempts to review the studies in this area done in India and a few from abroad that brought an evolution in the methodology of crop-weather modeling. [ABSTRACT FROM AUTHOR]

Agriculture planning plays a significant growth and food security of an agro-based country like India. In this Review we present a comprehensive and critical survey on current challenges and methodologies applied for various image processing and Machine learning approaches on variety of crops in their productivity increase, considering the following measures: Early detection/recognition of crop diseases, diagnosing methods and crop selection method in yield prediction. This paper presents an overview of existing reported techniques useful in detection of diseases in variety of crops. Finally we identify the challenges and some opportunities for future developments in this area. [ABSTRACT FROM AUTHOR]

Now a day's the trend shifted to conventional to organic most of the consumers are giving importance to Organic foods compare to the conventional foods. Every human-being run with technology for better life style. Food is the basic need of every living organism. In olden days we are using traditional methods to produce the food items and using natural fertilisers but now a days due to environmental changes the farmers are using chemicals to produce more quantity of crop output with this all the human beings facing so many health issues as well as it causes for environmental sustainability. Due to lack of time to cook they are going and purchasing readily available packaged food. The population is increased over the period of time the producers are also using chemicals and fertilisers to yield more crops to meet the demand of supply with this the consumers facing lot of health issues. At present days, the consumers required of healthy food are increased comparing to the last decade. In this research paper, the Researcher made an attempt to know the consumers opinion on Organic foods in Tirupathi Chittor district. For this purpose, first hand information was collected from 100 sample respondents and the collected data was analysed with the help of stastical tool such as Chi-Square. [ABSTRACT FROM AUTHOR]

Rice crop is one of the major cultivating crops in India, especially in south India. Fertiliser plays an important role to achieve the highest rice yield. Each crop needs an adequate amount of fertiliser for its growth. Inadequate and overdose of fertiliser leads to nutrient deficiency and lesser yield of crops. Rice has a unique fertiliser recommendation based on climatic conditions and soil types but the issue is the random use of fertilisers by the farmer, without knowing their own land soil nutrition. Soil consists of primary nutrients and secondary nutrients. This supplement is used for crop growth. This paper discusses a proposed methodology of an optimised fertiliser simulation model for Cuddalore and Salem district and its sub talukas for rice and maize crops. In this research work, the fuzzy logic technique is used because this is one of the best ways for problem-solving techniques. In this fuzzy logic, Mamdani fuzzy logic-based tool is used. The Mamdani fuzzy logic tool is used for the suggestion of the fertilisers required according to the minerals present in their soil. In result total 45 rules are created for recommending the fertiliser use for paddy and maize crops. [ABSTRACT FROM AUTHOR]

Machine learning techniques play an important role in solving real world problems. These techniques are also found to be successful in the field of Agriculture for crop yield prediction, leaf disease detection, fruit disease detection, vegetable quality assessment, etc. In this paper, the authors performed comparative analysis of various supervised machine learning techniques for crop yield prediction from soil parameters. Five supervised machine learning techniques such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RF) have been taken for the experimental analysis. The experiments have been carried out for the prediction of the three most commonly grown crops in India: Rice, Wheat and Mustard. The performance of each technique for every crop taken in this study, has been evaluated on the basis of four metrics i.e. accuracy, recall, precision and f-score. The experimental results revealed that decision tree and random forest performed better than all the other supervised machine learning techniques taken in this study, for the prediction of each crop. [ABSTRACT FROM AUTHOR]

Main challenge for agriculture development is to increase agriculture production and productivity of land. The green revolution brought impressive gains in food production with the helpee of chemical fertilizer, but with insufficient concern for sustainability. Uses of chemical fertilizer not only impoverished soils, destroyed ecological balances and lead to environment damage but also adversely affected human health. Bio-fertilizers are the best supplement of chemical fertilizers. They are alternative means of accessing plant nutrients without polluting environment with sustainability. Bio-fertilizers, more commonly known as microbial inoculants, are artificially multiplied cultures of certain soil organisms that can improve soil-fertility and crop productivity by increasing the activity of certain useful bacteria and also help in providing nutrients like nitrogen via fixation and phosphorus via increasing solubiity that are naturally abundant in soil or atmosphere but unusable for plants. This paper Significance of Bio-fertiizer in Context of Indian Economy' looks the uses of bio-fertilizer in context of Indian agriculture and also looks its effect on environment, soil fertility and productivity. Paper also enlightens affect of chemical fertilizers. Bio-fertilizers are very usuful but less popular in India, this paper also highlights the causes which are responsible for this, and there are also some suggestions for making them popular. Bio-fertilizer have various benefits. Beside accessing nutrients, different bio-fertilizers also provide growth-promoting factors Oxine, Jibreline and Indole acetic acid to plants and some have been successfully facilitating composting and effective recycling of solid wastes. By controlling soil borne diseases and improving the soil health and soil properties these organisms help not only in saving, but also in effectively utilizing chemical fertilizers and result in higher yield rates. Bio-fertilizers are not only effective for that particular crop in which they used, but also increased the production of next crops. Bio-fertilizers are cheap inputs, free from the environmentally adverse implications that chemical fertilizers have. Bio-fertilizers offer a new technology to Indian agricultuee holding a promise to balance many of the shortcomings of the conventional chemical based technology. Dependence on chemical fertilizers for future agriculture growth would mean further loss in soil quality, possibilities of water contamination and unsustainable burden on the fiscal system. Bio-fertilizers are cheap and have various benefits. It is very effective for sustainable agriculture development so the government of India has been trying to promote an improved practice involving use of bio-fertilizers along with fertilizers. [ABSTRACT FROM AUTHOR]

West Bengal Sericulture and rural areas are facing multiple socio-economic changes, including a transition from an agriculture-based to a service-based economy. This restructuring forces agricultural and rural sector-networks to reformulate their (self-) definitions. Farming is an important and key factor for the economy of developing countries. Since last several years, farmers once again welcomed many traditional farming systems to get crop yield for completion of food requirement. Although, traditional techniques are not much sufficient in new agriculture world because farming is getting new challenges from biotic and abiotic factors. New techniques and systems are getting popularity in present farming because of their important benefits which can solve all challenges on farming. Sericulture is a short gestation period labour intensive enterprise which can go a long way in promoting inclusive growth and alleviating poverty in rural areas. However, sericulture industry development in the state has been inactivated with a rapid change of its political and economic system and a large portion of sericulture farmers has consequently given up cocoon production activities thus to lose their job opportunities and income resources. The review paper indicates the current scenario, identifies the state’s existing potential and constraints and suggests future planning for the development of sericulture & silk industry in the state. [ABSTRACT FROM AUTHOR]

Water-harvesting structures have the potential to increase the productivity of arable lands by enhancing crop yields and by reducing the risk of crop failure in arid and semi-arid regions, where water shortages are common because of scanty rainfall and its uneven distribution. In semi-arid regions of Rajasthan, India, existing practice of harvesting rainwater is through anicut and earthen embankments. Because of higher costs and higher technical skills involved in the construction of these structures, these structures have not been accepted by the resource-poor local people. Therefore, in the present paper, the detailed design of some low-cost water-harvesting structures using locally available materials and adaptable to the socio-economic conditions of the beneficiaries is discussed. Two types of cost-effective water-harvesting structures, which include dry stone masonry and upstream-wall cement masonry of heights 1, 2, and 2.5 m for catchments of less than 10, 10 to 20, and 20 to 30 ha, respectively are proposed and designed. The analysis of costs involved in constructing dry stone masonry and upstream-wall cement masonry water-harvesting structures, emergency spillway, anicuts and earthen embankments revealed that the earthen embankments have the least cost of construction whereas the anicuts have the highest construction costs for all the selected heights. However, based on the past experiences, earthen embankments are not suitable for the semi-arid regions of Rajasthan. The economic evaluation of the proposed structures indicated that the dry stone masonry structures are very cost-effective for the region with a benefit-cost ratio of 3.5:1 and the net present worth value of Rs. 102978. Although the economic indicators ranked the upstream-wall cement masonry structures lower than the dry stone masonry structures, the former has greater stability and strength compared to the latter. In practice, both the cost-effective water-harvesting structures (i.e., dry stone masonry and upstream-wall cement masonry) are gaining wide acceptance and popularity in the region through some nongovernmental organizations, which have adopted the design presented in this paper. [ABSTRACT FROM AUTHOR]

Agriculture is a significant contributor to India's economic growth. The rising population of country and constantly changing climatic conditions have an impact on crop production and food security. A variety of factors influence crop selection, including market price, production rate, soil type, rainfall, temperature, government policies, etc. Many changes are required in the agricultural sector in order to enhance the Indian economy. In this research work authors have implemented various machine learning techniques to estimate the crop yield in Rajasthan state of India on five identified crops. The results indicate that among all the applied algorithms; Random Forest, SVM, Gradient Descent, long short-term memory, and Lasso regression techniques; the random forest performed better than others with 0.963 R2, 0.035 RMSE, and 0.0251 MAE. The results were validated using R2, root mean squared error, and the mean absolute error to cross-validation techniques. This paper intends to put the crop selection method into practice to help farmers solve crop yield problems. [ABSTRACT FROM AUTHOR]

The idea of a 'good farmer' who adheres to scientific methods of crop production and produces high yields was commonly articulated by scientists and some farmers in Malwa, central India, while evaluating soybean farming. However, through detailed ethnographic description of the everyday practices of soybean farmers in Malwa, this paper argues that there is no direct link between scientific methods and high productivity, defined as yield per unit of land, because the outcome is dependent on variables that cannot be controlled by farmers. Rather, the metaphor of a 'good farmer' was selectively used by upper-caste farmers to reinforce their power and authority in the face of declining state support to emphasize their continued importance to agriculture and the state. When upper-caste farmers were less productive, they placed the blame on the environment, but when lower-caste farmers produced less, the blame was placed on their (lack of) capability to understand scientific techniques that were expected to increase productivity. This paper suggests that instead of using the productivity discourse to evaluate farming, a different valuation reveals the positive contribution of soybean as a poor farmer's crop due to its agronomic characteristics and market environment. [ABSTRACT FROM AUTHOR]

Tea is one of the major economic crops of India. People use tiny tea leaves to make beverages. The diseases in tea leaves affect the quality and yield of this cultivation. This paper proposes a disease classification model to prevent the major loss in crop yield. Tea leaf images are captured using a camera, and various image processing techniques are applied to the images to identify which disease is affected. The proposed model works for three major tea leaf diseases: blister blight, scab, and spot. The model extracts the Haralick features using Gray Level Co-occurrence Matrix (GLCM), and the most relevant features are selected with the help of the metaheuristic optimization technique. Fuzzy Rough Nearest Neighbor (FRNN) is used for classification techniques, and the model gave better accuracy than other existing techniques. [ABSTRACT FROM AUTHOR]

Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively. [ABSTRACT FROM AUTHOR]

Food security is a crucial requirement in today's world to meet the dietary needs of individuals. As the population continues to grow, the demand for food will increase by 70 to 100 percent by 2050. Therefore, there is an urgent need to develop an approach that can assist farmers in predicting crop yield accurately and in a timely manner before crop harvesting. In this direction, the present study proposed a nature-inspired optimized hybrid convolutional neural network with bidirectional long short-term memory to extract the nonlinear complex relationships among crop attributes. This hybrid deep learning model was optimized using a particle swarm optimization approach to automate the selection of appropriate hyperparameters for wheat yield prediction. The proposed model was developed to estimate wheat yield in a major wheat-producing state in India from 2000 to 2018 using the temporal and spatial characteristics of wheat crops. The experiment was conducted by integrating the historical yield, meteorological data, remote sensing-derived indices, and soil parameters from the October to April season. Furthermore, we also evaluated the performance of the proposed model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE) with publicly available datasets such as Agro_data, Soybean_data, and FAO_data. The experimental results showed that the proposed PSO-CNN-Bi-LSTM method achieved an MAE of 0.39, MSE of 0.18, and RMSE of 0.42 (tonnes/ha) and outperformed existing CNN, LSTM, CNN-LSTM, CNN-Bi-LSTM, CNN-LSTM-PSO, CNN-Bi-LSTM-BO-GO, and neural network methods. Our findings demonstrated that the proposed methodology could be a promising alternative for predicting crop yield. [ABSTRACT FROM AUTHOR]

Ginger (Zingiber officinale Roscoe) is an important spice crop in India, which is also one of the leading producer and exporter of ginger in the world. During cultivation, the crop is severely infected by various diseases of them soft rot, yellows, Phyllosticta leaf spot, storage rot, bacterial wilt, mosaic, chlorotic fleck are important. These diseases reduce the potential yields drastically. The geographical distribution, losses, symptoms, causal organism, disease cycle, epidemiology and host resistance, cultural, biological, chemical and integrated management of above mentioned diseses have been discussed in the present paper. [ABSTRACT FROM AUTHOR]

Deep learning techniques have been used effectively for rubber crop yield prediction. A hybrid of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is the best technique for crop yield prediction because it can effectively handle uncertainty of features. Hence, in this paper, a hybrid CNNRNN method is proposed to forecast Hevea yields based on environmental data in Kerala state, India. The proposed hybrid CNN-RNN method reduces the internal covariate shift of CNN by batch normalization and solves the gradient vanishing or exploding problem of RNN using LSTM with a cell activation mechanism. The proposed method has three essential characteristics: (i) it captures the time dependency of environmental factors and improves the inherent computational time; (ii) it is capable of generalizing the yield prediction under uncertain conditions without loss of prediction accuracy; (iii) combined with the back propagation and feed forward method it can reveal the extent to which samples of weather conditions and soil data conditions are suitable to provide a clear boundary between rubber yield variations. [ABSTRACT FROM AUTHOR]

India is the world's largest edible oil importer, and soybean oil accounts for a major portion of those imports, with implications for the Indian economy. Despite being the 4th largest globally in terms of harvested soybean area and 5th largest in terms of production, India is still heavily dependent on imports to meet the vegetable oil requirement for its population. It is therefore imperative to understand the dynamics and trends in India's soybean production to help the country achieve self-sufficiency in edible oils. This study provides the first spatially explicit analysis of soybean in India, using long-term spatial and temporal statistics at national and subnational levels, using spatial and temporal statistical analysis models to examine the historical trends and its future prospects. Our analysis details the overall soybean expansion across the country and the increase in production but we also note that the annual growth rate has declined in each consecutive decade even though the area continues to expand. The average national yield has been stagnant at around 1 T/Ha but for some of the low-producing districts, a higher yield of more than 3 T/ha is reported. For most major producing districts, soybean yields are below 1.5 T/Ha. The state of Madhya Pradesh which was the major soybean producer is now matched by the state of Maharashtra in terms of production, however, Madhya Pradesh still has the largest area under soybean. We analyzed soybean hotspot expansion in India and found that the mean center of the soybean area and production has shifted approximately 93 km towards the south and 24 km to the west as the crop is rapidly being adopted in the southern and western parts of India expanding the hotspot in these parts. District-level analysis showed that the total number of districts constituting hotspots of soybean cultivation in India has increased from 29 to 42 in three decades. Furthermore, analysis of soybean oil and meal consumption with respect to the national population, import, export, domestic production, GDP per capita, and price of soybean oil and meal suggests that soybean oil and meal are highly correlated with GDP per capita and population, indicating that consumption of soybean oil and meal is likely to increase as GDP per capita increases, and future demand is expected to rise with the anticipated growth in the Indian population. Increased soybean production can play a significant role in increasing national food security for India and reducing dependence on foreign oil imports and also help the economy with soy meal exports. Understanding the spatiotemporal variability in area and yield will help target interventions to increase production. Given the overall low yields with high variability in production, particularly in recent years primarily due to successive extreme rains and droughts in major producing districts and the overall need to increase production to meet the country's demand, there is a pressing need for government policies and research aimed at narrowing the yield gap and developing soybean varieties that are more productive and resilient to climate change. [ABSTRACT FROM AUTHOR]

This study uses the Just–Pope approach to investigate the effects of seasonal weather variables and extremes on the mean yield and yield variability of rice, bajra, chickpea, groundnut and sugarcane in India during the period 1990–2015. Results reveal that changes in rainfall and evapotranspiration across seasons largely affect mean yields for most crops, including bajra, chickpea and groundnut. However, high summer rainfall and low monsoon evapotranspiration extremes reduce groundnut and chickpea yield variability. Considering the importance of water availability to crop yields, this study suggests improving irrigation and water reallocation and management to reduce the severity of seasonal climate effects. [ABSTRACT FROM AUTHOR]

India's rice production has come under pressure from a number of biotic and abiotic stresses, resulting in a significant deceleration in its productivity growth in recent years. Hybrid rice technology is considered a sustainable option for boosting productivity growth. The adoption rate of hybrid rice technology, however, has remained sluggish. This paper, using data from a large-scale, nationally representative survey of farm households, identifies causes of low adoption of hybrid rice technology and subsequently assesses the impact of low adoption on crop yield. Our findings demonstrate that in India, hybrid rice is often grown on relatively poor soils, resulting in greater costs for irrigation as well as for other inputs, such as fertilizers, essential for growth. Although hybrid rice technology appears to be scale neutral, farmers' poor access to information on its biochemical traits and agronomic practices, as well as poor access to financial resources, hampers scaling up its adoption. More important, our findings reveal that the relative yield advantage of hybrids over open-pollinated modern varieties is not large enough to incentivize rapid adoption of hybrid rice technology. [ABSTRACT FROM AUTHOR]

Natural Farming represents an agro-ecological methodology for farming that emphasizes regenerative practices with an aim to promote holistic ecological balance and reduce the dependence on external inputs as well as financial resources. Substantial concern has recently arisen over the need to promote agroecosystems that are more sustainable in order to improve the deteriorating soil health as well as reversing the yield plateau of crop. So, the current on farm field experiment was executed comprising of 8 treatments with different combination of natural farming inputs (Ghanjeevamrit, Jeevamrit, Beejamrit), organic fertilizer (such as FYM), integrated nutrient management (NPK, FYM, Azotobacter and Azolla) and in-organic(NPK) to examine and compare the consequence of natural farming inputs, organic fertilizer and in-organic dosage of fertilizer on soil nitrogen uptake, soil physicochemical properties, soil biological properties, soil microbial population and crop yields in a rice-wheat cropping system over two crop seasons 2021–23 [rice (Pusa-1509) and wheat (HD-3086)]. The study results demonstrated that there was significant (p  <  0.05) increase in the soil’s nitrogen availability and nitrogen uptake with the use of natural farming inputs as compared to control treatment, whereas, natural farming treatments (TNF1, TNF2, TNF3, TMNF) were inferior than integrated nutrient management (TINM) and recommended doses of fertilizer (TRDF) treatment in case of nitrogen uptake by both rice and wheat crop. The soil enzymatic activity (Dehydrogenase, β-glucosidase, and urease), soil microbial biomass carbon and nitrogen, and soil microbial population (Bacteria, fungi, and actinomycetes) were significantly (p  <  0.05) higher in treatment receiving natural farming inputs compare to in-organic fertilizer and organic fertilizer. A positive and significant correlation was observed between potential mineralization nitrogen and soil enzymatic activity (Dehydrogenase, β-glucosidase, and urease), soil microbial biomass carbon and nitrogen and soil microbial population (Bacteria, fungi, and actinomycetes). The crop yield at the end of experiment recorded to be highest in treatment TINM (75% RDF (In-organic)  +  25% RDF (FYM)  +  BGA) i.e., (Rice4.76  t/ha and Wheat- 5.82  t/ha) compared to TRDF and TNF. A crop yield reduction of 14.2% was observed in treatment receiving natural farming inputs compare to TINM. A significant increase in crop yield was observed in TMNF (Jeevamrit (25%)  +  Ghanjeevamrit (25%)  +  50% RDF through FYM  +  Beejamrit) compare to Tc (Control) and TFYM (Farmyard manure). Therefore, our study suggests that adoption of natural farming inputs over time can facilitate the enhancement of soil biological health of Inceptisol of Trans Gangetic Plain of India. [ABSTRACT FROM AUTHOR]

Farming and agriculture are the oldest professions, but they are adapting to the technology revolution to accommodate the world's growing population. UAV technology is part of the agriculture revolution, which aims to boost crop yields, properly monitor fields, and handle manpower shortages and resource efficiency. Rural India's tiny farmers cannot afford UAV technology; therefore, it has not yet spread. Payload capacity, endurance, and selective spraying are other considerations. Thus, a low-cost, long-lasting UAV is necessary. This study modified the arm assembly to create a cheap hexacopter UAV. The endurance increased by 10% when 1.5 kg was lost. ABS plastic was used to make the modular arm. For working loads of 9 kg and 10 kg, pesticide/fertilizer spraying saves time, money, and manpower. Thus, a pressure-area coverage-cone angle connection is needed. This study examined spray patterns at different pressures and heights by varying flat fan nozzle and complete cone nozzle orifice diameters. These factors were linked, helping farmers choose the right nozzle. This nozzle was installed in the UAV and field-tested for paddy crops, showing a significant production improvement and lower operational cost. Chemical use pollutes and leaves traces in produce. Precision farming with artificial intelligence (AI) has solved this problem. In this experiment, AI algorithms were used to lemon leaves. Three AI systems were tested on different datasets to forecast plant stress by analyzing leaves due to technical constraints. CNN's accuracy and computing speed make it ideal for precision farming. This work's UAV was 30% cheaper than commercial UAVs and had more durability. Farmers will also benefit from the flat fan and complete cone nozzles' pressure-area coverage connection. [ABSTRACT FROM AUTHOR]

Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) has significantly affected maize crop yields, production efficiency, and farmers' incomes in the Indian Eastern Gangetic Plains region since it was first observed in India in 2018. A lack of awareness by maize growers of the appropriate selection, method, and timing of insecticide application not only creates a barrier to sustainable FAW control but also contributes to increased environmental pollution, reduced human health and increased production costs. We demonstrated that FAW inflicted the most damage in early whorl growth stage of maize, regardless of whether chemical insecticides were applied. FAW egg masses and larvae collected from maize fields in which no insecticides had been sprayed showed high parasitism rates by parasitoid wasps; in contrast fields that had been sprayed had much lower rates of parasitism on FAW. Ten hymenopteran parasitoids were observed in maize fields across the study region, suggesting a diversity of natural methods to suppress FAW in maize at different growth stages. These included two FAW egg parasitoids and eight FAW larval parasitoids. Microplitis manilae Ashmead was the most abundant FAW larval parasitoid species, and Telenomus cf. remus was the dominant FAW egg parasitoid species. Endemic FAW parasitoids such as those observed in this study have great potential as part of a sustainable, cost-effective agroecological management strategy, which can be integrated with other methods to achieve effective control of FAW. [ABSTRACT FROM AUTHOR]

Background: Drought is one of the important abiotic stresses that can significantly reduce crop yields. In India, about 24% of Brassica juncea (Indian mustard) cultivation is taken up under rainfed conditions, leading to low yields due to moisture deficit stress. Hence, there is an urgent need to improve the productivity of mustard under drought conditions. In the present study, a set of 87 B. carinata-derived B. juncea introgression lines (ILs) was developed with the goal of creating drought-tolerant genotypes. Method: The experiment followed the augmented randomized complete block design with four blocks and three checks. ILs were evaluated for seed yield and its contributing traits under both rainfed and irrigated conditions in three different environments created by manipulating locations and years. To identify novel genes and alleles imparting drought tolerance, Quantitative Trait Loci (QTL) analysis was carried out. Genotyping-by-Sequencing (GBS) approach was used to construct the linkage map. Results: The linkage map consisted of 5,165 SNP markers distributed across 18 chromosomes and spanning a distance of 1,671.87 cM. On average, there was a 3.09 cM gap between adjoining markers. A total of 29 additive QTLs were identified for drought tolerance; among these, 17 (58.6% of total QTLs detected) were contributed by B. carinata (BC 4), suggesting a greater contribution of B. carinata towards improving drought tolerance in the ILs. Out of 17 QTLs, 11 (64.7%) were located on the B genome, indicating more introgression segments on the B genome of B. juncea. Eight QTL hotspots, containing two or more QTLs, governing seed yield contributing traits, water use efficiency, and drought tolerance under moisture deficit stress conditions were identified. Seventeen candidate genes related to biotic and abiotic stresses, viz., SOS2, SOS2 like, NPR1, FAE1-KCS, HOT5, DNAJA1, NIA1, BRI1, RF21, ycf2, WRKY33, PAL, SAMS2, orf147, MAPK3, WRR1 and SUS, were reported in the genomic regions of identified QTLs. Conclusions: The significance of B. carinata in improving drought tolerance and WUE by introducing genomic segments in Indian mustard is well demonstrated. The findings also provide valuable insights into the genetic basis of drought tolerance in mustard and pave the way for the development of drought-tolerant varieties. [ABSTRACT FROM AUTHOR]

In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield. [ABSTRACT FROM AUTHOR]

Advance prediction of crop yield is very critical in the context of ensuring food security as the region specific challenges in social and environmental conditions often infringe plan of policy makers. This study presents a generic methodology to configure and fine tune the state-of-the-art Long Short-Term Memory (LSTM) based Deep Learning (DL) model through hyperparameter optimization for prediction of yield (annual crop production) in Wheat, Groundnut and Barely over India based on multiple independent input variables identified using multicollinearity test. The Monte Carlo cross-validation method is used to validate the optimized LSTM models. Results from the LSTM model tuning showed that among the 4 optimizers tested, Adam was found to perform better irrespective of the crop and Bi-LSTM outperformed sLSTM in terms of prediction accuracy. The percentage reduction in error with Bi-LSTM compared to sLSTM in predicting wheat and groundnut crop yield was 39% and 13% respectively while in case of barley crop, error reduction was marginal (0.34%). The performance of optimized Bi-LSTM model is compared with the performance of traditional machine learning (ML) models such as support vector regression (SVR) and SVR polynomial {2nd and 3rd order}, Auto Regressive Integrated Moving Average (ARIMA) and ARIMAX (ARIMA with exogenous variables) and Vector Auto-regression (VAR). The Bi-LSTM model is found to be superior to ML models; the percentage reduction in mean absolute scaled error with the Bi-LSTM compared to the best performing ML model was 94%, 72%, and 71% in predicting wheat, groundnut and barley yield respectively. This study showed that by choosing proper explanatory (independent) variable and hyperparameter optimization, a simple (single layer) structure of deep neural network (LSTM) outperformed traditional ML models in terms of accuracy for crop yield prediction application. [ABSTRACT FROM AUTHOR]

Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study's findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers. [ABSTRACT FROM AUTHOR]

Farmers in India have benefited considerably from the combination of satellite-enabled services and data obtained on the ground. The India Meteorological Department, Ministry of Earth Sciences, provides weather forecasting, agro-advisory services, agromet services, soil moisture monitoring, and agricultural extension initiatives to encourage agricultural operations in India. Indian Space Research Organization's (ISRO) also partners with the Ministry of Agriculture and Farmers Welfare on several satellite data and geographic information systems-based agricultural applications. ISRO, in collaboration with the Ministry of Agriculture and Farmers Welfare, has developed applications including horticultural crop inventory and site suitability for expansion in unutilized places; crop assessment using medium- and high-resolution satellite data; field information gathering with field photos using a smartphone application; and crop cutting experiments based on satellite-derived crop vigor information. ISRO has provided technology for FASAL and the National Agricultural Drought Assessment and Surveillance System to the Department of Agriculture Cooperation and Farmer Welfare. ISRO has also incorporated the Central Water Commission's monitoring of irrigation systems. Overall, satellite-enabled services have transformed agricultural operations in India by providing farmers with precise and timely data that enable them to make educated decisions about their crops, resulting in increased crop yields and financial returns. [ABSTRACT FROM AUTHOR]